diff --git a/main/404.html b/main/404.html
index f5046860..077ddc53 100644
--- a/main/404.html
+++ b/main/404.html
@@ -50,12 +50,6 @@
 <li class="nav-item">
   <a class="nav-link" href="https://hta-pharma.github.io/maicplus/news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul>
 <form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="search.json" id="search-input" placeholder="Search for" autocomplete="off">
diff --git a/main/articles/index.html b/main/articles/index.html
index f73cefbd..c0b0d245 100644
--- a/main/articles/index.html
+++ b/main/articles/index.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/articles/using_maicplus.html b/main/articles/using_maicplus.html
index 984252ed..02fb043e 100644
--- a/main/articles/using_maicplus.html
+++ b/main/articles/using_maicplus.html
@@ -52,12 +52,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul>
 <form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off">
diff --git a/main/authors.html b/main/authors.html
index c1d7ec96..c96971e5 100644
--- a/main/authors.html
+++ b/main/authors.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/index.html b/main/index.html
index 51f17748..7436d95a 100644
--- a/main/index.html
+++ b/main/index.html
@@ -52,12 +52,6 @@
 <li class="nav-item">
   <a class="nav-link" href="news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul>
 <form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="search.json" id="search-input" placeholder="Search for" autocomplete="off">
diff --git a/main/news/index.html b/main/news/index.html
index fcf740c2..c8e8ad25 100644
--- a/main/news/index.html
+++ b/main/news/index.html
@@ -35,12 +35,6 @@
 <li class="active nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/pkgdown.yml b/main/pkgdown.yml
index a884c7f0..30d35836 100644
--- a/main/pkgdown.yml
+++ b/main/pkgdown.yml
@@ -3,7 +3,7 @@ pkgdown: 2.0.9
 pkgdown_sha: ~
 articles:
   using_maicplus: using_maicplus.html
-last_built: 2024-06-21T13:25Z
+last_built: 2024-07-12T14:06Z
 urls:
   reference: https://hta-pharma.github.io/maicplus/reference
   article: https://hta-pharma.github.io/maicplus/articles
diff --git a/main/reference/Rplot002.png b/main/reference/Rplot002.png
index 88c99fe3..10269516 100644
Binary files a/main/reference/Rplot002.png and b/main/reference/Rplot002.png differ
diff --git a/main/reference/Rplot003.png b/main/reference/Rplot003.png
index b3941f4e..5499529c 100644
Binary files a/main/reference/Rplot003.png and b/main/reference/Rplot003.png differ
diff --git a/main/reference/Rplot004.png b/main/reference/Rplot004.png
index 3b699623..9b875c91 100644
Binary files a/main/reference/Rplot004.png and b/main/reference/Rplot004.png differ
diff --git a/main/reference/Rplot005.png b/main/reference/Rplot005.png
index 4ec0cc2d..69eebefc 100644
Binary files a/main/reference/Rplot005.png and b/main/reference/Rplot005.png differ
diff --git a/main/reference/adrs_sat.html b/main/reference/adrs_sat.html
index ac54c1a6..a76a8f64 100644
--- a/main/reference/adrs_sat.html
+++ b/main/reference/adrs_sat.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/adrs_twt.html b/main/reference/adrs_twt.html
index 596075ff..e0cba1d7 100644
--- a/main/reference/adrs_twt.html
+++ b/main/reference/adrs_twt.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/adsl_sat.html b/main/reference/adsl_sat.html
index a0022b82..3c5b1c13 100644
--- a/main/reference/adsl_sat.html
+++ b/main/reference/adsl_sat.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/adsl_twt.html b/main/reference/adsl_twt.html
index 6dae9c98..35c4c975 100644
--- a/main/reference/adsl_twt.html
+++ b/main/reference/adsl_twt.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
@@ -105,7 +99,8 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="adtte_twt.html">adtte_twt</a></code>,
 <code><a href="agd.html">agd</a></code>,
 <code><a href="centered_ipd_twt.html">centered_ipd_twt</a></code>,
-<code><a href="pseudo_ipd_twt.html">pseudo_ipd_twt</a></code></p></div>
+<code><a href="pseudo_ipd_twt.html">pseudo_ipd_twt</a></code>,
+<code><a href="weighted_twt.html">weighted_twt</a></code></p></div>
     </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/main/reference/adtte_sat.html b/main/reference/adtte_sat.html
index cc3e9716..81e297d1 100644
--- a/main/reference/adtte_sat.html
+++ b/main/reference/adtte_sat.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/adtte_twt.html b/main/reference/adtte_twt.html
index 6c81bdbf..8a43830f 100644
--- a/main/reference/adtte_twt.html
+++ b/main/reference/adtte_twt.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
@@ -108,7 +102,8 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="adsl_twt.html">adsl_twt</a></code>,
 <code><a href="agd.html">agd</a></code>,
 <code><a href="centered_ipd_twt.html">centered_ipd_twt</a></code>,
-<code><a href="pseudo_ipd_twt.html">pseudo_ipd_twt</a></code></p></div>
+<code><a href="pseudo_ipd_twt.html">pseudo_ipd_twt</a></code>,
+<code><a href="weighted_twt.html">weighted_twt</a></code></p></div>
     </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/main/reference/agd.html b/main/reference/agd.html
index 3bf2cf11..00142eba 100644
--- a/main/reference/agd.html
+++ b/main/reference/agd.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
@@ -118,7 +112,8 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="adsl_twt.html">adsl_twt</a></code>,
 <code><a href="adtte_twt.html">adtte_twt</a></code>,
 <code><a href="centered_ipd_twt.html">centered_ipd_twt</a></code>,
-<code><a href="pseudo_ipd_twt.html">pseudo_ipd_twt</a></code></p></div>
+<code><a href="pseudo_ipd_twt.html">pseudo_ipd_twt</a></code>,
+<code><a href="weighted_twt.html">weighted_twt</a></code></p></div>
     </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/main/reference/basic_kmplot.html b/main/reference/basic_kmplot.html
index 54b7f813..b5992c6e 100644
--- a/main/reference/basic_kmplot.html
+++ b/main/reference/basic_kmplot.html
@@ -39,12 +39,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/basic_kmplot2.html b/main/reference/basic_kmplot2.html
index e3a60e37..b0fa33c6 100644
--- a/main/reference/basic_kmplot2.html
+++ b/main/reference/basic_kmplot2.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/bootstrap_HR.html b/main/reference/bootstrap_HR.html
index d5e0bd1d..7a1d9de9 100644
--- a/main/reference/bootstrap_HR.html
+++ b/main/reference/bootstrap_HR.html
@@ -41,12 +41,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/bucher.html b/main/reference/bucher.html
index 897451af..1159aee8 100644
--- a/main/reference/bucher.html
+++ b/main/reference/bucher.html
@@ -47,12 +47,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/calculate_weights_legend.html b/main/reference/calculate_weights_legend.html
index 371516c0..ff7e72f3 100644
--- a/main/reference/calculate_weights_legend.html
+++ b/main/reference/calculate_weights_legend.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/center_ipd.html b/main/reference/center_ipd.html
index e76b58ed..b3d392f1 100644
--- a/main/reference/center_ipd.html
+++ b/main/reference/center_ipd.html
@@ -39,12 +39,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/centered_ipd_sat.html b/main/reference/centered_ipd_sat.html
index 11887cd5..4f23a52e 100644
--- a/main/reference/centered_ipd_sat.html
+++ b/main/reference/centered_ipd_sat.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/centered_ipd_twt.html b/main/reference/centered_ipd_twt.html
index d03cc729..45841214 100644
--- a/main/reference/centered_ipd_twt.html
+++ b/main/reference/centered_ipd_twt.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
@@ -128,7 +122,8 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="adsl_twt.html">adsl_twt</a></code>,
 <code><a href="adtte_twt.html">adtte_twt</a></code>,
 <code><a href="agd.html">agd</a></code>,
-<code><a href="pseudo_ipd_twt.html">pseudo_ipd_twt</a></code></p></div>
+<code><a href="pseudo_ipd_twt.html">pseudo_ipd_twt</a></code>,
+<code><a href="weighted_twt.html">weighted_twt</a></code></p></div>
     </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/main/reference/check_weights.html b/main/reference/check_weights.html
index f73cc20b..2e877dc1 100644
--- a/main/reference/check_weights.html
+++ b/main/reference/check_weights.html
@@ -37,12 +37,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/complete_agd.html b/main/reference/complete_agd.html
index 4bceb45e..95211a4f 100644
--- a/main/reference/complete_agd.html
+++ b/main/reference/complete_agd.html
@@ -39,12 +39,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/dummize_ipd.html b/main/reference/dummize_ipd.html
index cff868e1..ba01f015 100644
--- a/main/reference/dummize_ipd.html
+++ b/main/reference/dummize_ipd.html
@@ -39,12 +39,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/ess_footnote_text.html b/main/reference/ess_footnote_text.html
index 8e38ade5..80ffea20 100644
--- a/main/reference/ess_footnote_text.html
+++ b/main/reference/ess_footnote_text.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/estimate_weights.html b/main/reference/estimate_weights.html
index 1376e760..326976f7 100644
--- a/main/reference/estimate_weights.html
+++ b/main/reference/estimate_weights.html
@@ -49,12 +49,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/ext_tte_transfer.html b/main/reference/ext_tte_transfer.html
index 0c26864b..e84d5dfd 100644
--- a/main/reference/ext_tte_transfer.html
+++ b/main/reference/ext_tte_transfer.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/find_SE_from_CI.html b/main/reference/find_SE_from_CI.html
index 4c05e8bd..41a4d01c 100644
--- a/main/reference/find_SE_from_CI.html
+++ b/main/reference/find_SE_from_CI.html
@@ -51,12 +51,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/generate_survival_data.html b/main/reference/generate_survival_data.html
index 3debd60c..e5f6b05f 100644
--- a/main/reference/generate_survival_data.html
+++ b/main/reference/generate_survival_data.html
@@ -47,12 +47,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/get_pseudo_ipd_binary.html b/main/reference/get_pseudo_ipd_binary.html
index 58107c88..fe6bfc9d 100644
--- a/main/reference/get_pseudo_ipd_binary.html
+++ b/main/reference/get_pseudo_ipd_binary.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/get_time_as.html b/main/reference/get_time_as.html
index ea2ccd8c..c4121b0e 100644
--- a/main/reference/get_time_as.html
+++ b/main/reference/get_time_as.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/index.html b/main/reference/index.html
index 44799e57..2934772d 100644
--- a/main/reference/index.html
+++ b/main/reference/index.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
@@ -276,6 +270,11 @@ <h2 id="all-functions">All functions<a class="anchor" aria-label="anchor" href="
           <code><a href="weighted_sat.html">weighted_sat</a></code> 
         </dt>
         <dd>Weighted object for single arm trial data</dd>
+      </dl><dl><dt>
+          
+          <code><a href="weighted_twt.html">weighted_twt</a></code> 
+        </dt>
+        <dd>Weighted object for two arm trial data</dd>
       </dl></div>
   </main></div>
 
diff --git a/main/reference/kmplot.html b/main/reference/kmplot.html
index 21cf738f..ea1136ef 100644
--- a/main/reference/kmplot.html
+++ b/main/reference/kmplot.html
@@ -37,12 +37,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
@@ -3539,7 +3533,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 362  17.6230889     1   C</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 363 115.5534530     1   C</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 364  82.5114275     1   C</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 365 186.3398042     1   C</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 365 186.3398043     1   C</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 366  30.1885913     1   C</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 367  30.0502707     1   C</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 368  48.9121396     1   C</span>
diff --git a/main/reference/kmplot2-1.png b/main/reference/kmplot2-1.png
index 3f50e5a2..81e80fa3 100644
Binary files a/main/reference/kmplot2-1.png and b/main/reference/kmplot2-1.png differ
diff --git a/main/reference/kmplot2-2.png b/main/reference/kmplot2-2.png
index 465440fa..02d94284 100644
Binary files a/main/reference/kmplot2-2.png and b/main/reference/kmplot2-2.png differ
diff --git a/main/reference/kmplot2-3.png b/main/reference/kmplot2-3.png
index 9ddbd7b8..b7c61686 100644
Binary files a/main/reference/kmplot2-3.png and b/main/reference/kmplot2-3.png differ
diff --git a/main/reference/kmplot2-4.png b/main/reference/kmplot2-4.png
index 74efc5d2..0cd7ef91 100644
Binary files a/main/reference/kmplot2-4.png and b/main/reference/kmplot2-4.png differ
diff --git a/main/reference/kmplot2.html b/main/reference/kmplot2.html
index bbff1fbc..830331fc 100644
--- a/main/reference/kmplot2.html
+++ b/main/reference/kmplot2.html
@@ -39,12 +39,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/maic_anchored.html b/main/reference/maic_anchored.html
index f7742c89..ecbde7ff 100644
--- a/main/reference/maic_anchored.html
+++ b/main/reference/maic_anchored.html
@@ -37,12 +37,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/maic_unanchored.html b/main/reference/maic_unanchored.html
index 7584f7f4..5be51208 100644
--- a/main/reference/maic_unanchored.html
+++ b/main/reference/maic_unanchored.html
@@ -37,12 +37,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/maicplus-package.html b/main/reference/maicplus-package.html
index af491309..d18daf21 100644
--- a/main/reference/maicplus-package.html
+++ b/main/reference/maicplus-package.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/medSurv_makeup.html b/main/reference/medSurv_makeup.html
index e3458bec..f7baf4fa 100644
--- a/main/reference/medSurv_makeup.html
+++ b/main/reference/medSurv_makeup.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/ph_diagplot.html b/main/reference/ph_diagplot.html
index 74c74c5d..ed787c8f 100644
--- a/main/reference/ph_diagplot.html
+++ b/main/reference/ph_diagplot.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/ph_diagplot_lch.html b/main/reference/ph_diagplot_lch.html
index 889503ab..1cdb9f10 100644
--- a/main/reference/ph_diagplot_lch.html
+++ b/main/reference/ph_diagplot_lch.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/ph_diagplot_schoenfeld.html b/main/reference/ph_diagplot_schoenfeld.html
index 579525c6..a1f1021a 100644
--- a/main/reference/ph_diagplot_schoenfeld.html
+++ b/main/reference/ph_diagplot_schoenfeld.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/plot_weights_base.html b/main/reference/plot_weights_base.html
index 1016987d..11fdf0c2 100644
--- a/main/reference/plot_weights_base.html
+++ b/main/reference/plot_weights_base.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/plot_weights_ggplot.html b/main/reference/plot_weights_ggplot.html
index 844499d6..5e4ecced 100644
--- a/main/reference/plot_weights_ggplot.html
+++ b/main/reference/plot_weights_ggplot.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/process_agd.html b/main/reference/process_agd.html
index d65d1f3a..bd264475 100644
--- a/main/reference/process_agd.html
+++ b/main/reference/process_agd.html
@@ -51,12 +51,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/pseudo_ipd_sat.html b/main/reference/pseudo_ipd_sat.html
index ed042908..a066ea74 100644
--- a/main/reference/pseudo_ipd_sat.html
+++ b/main/reference/pseudo_ipd_sat.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/pseudo_ipd_twt.html b/main/reference/pseudo_ipd_twt.html
index e0a5ac40..47fab0d2 100644
--- a/main/reference/pseudo_ipd_twt.html
+++ b/main/reference/pseudo_ipd_twt.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
@@ -90,7 +84,8 @@ <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"
 <code><a href="adsl_twt.html">adsl_twt</a></code>,
 <code><a href="adtte_twt.html">adtte_twt</a></code>,
 <code><a href="agd.html">agd</a></code>,
-<code><a href="centered_ipd_twt.html">centered_ipd_twt</a></code></p></div>
+<code><a href="centered_ipd_twt.html">centered_ipd_twt</a></code>,
+<code><a href="weighted_twt.html">weighted_twt</a></code></p></div>
     </div>
 
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/main/reference/report_table_binary.html b/main/reference/report_table_binary.html
index 599ea402..81ed3a98 100644
--- a/main/reference/report_table_binary.html
+++ b/main/reference/report_table_binary.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/report_table_tte.html b/main/reference/report_table_tte.html
index f21bd4f0..47b26bc5 100644
--- a/main/reference/report_table_tte.html
+++ b/main/reference/report_table_tte.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/survfit_makeup.html b/main/reference/survfit_makeup.html
index 0fa42374..0a60e5d0 100644
--- a/main/reference/survfit_makeup.html
+++ b/main/reference/survfit_makeup.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/time_conversion.html b/main/reference/time_conversion.html
index a8381ef3..80d01278 100644
--- a/main/reference/time_conversion.html
+++ b/main/reference/time_conversion.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/weighted_sat.html b/main/reference/weighted_sat.html
index e144db9b..1d0793d5 100644
--- a/main/reference/weighted_sat.html
+++ b/main/reference/weighted_sat.html
@@ -35,12 +35,6 @@
 <li class="nav-item">
   <a class="nav-link" href="../news/index.html">Changelog</a>
 </li>
-<!-- start dropdown for versions --> <li class="nav-item dropdown">
-        <a href="#" class="nav-link dropdown-toggle"
-          data-bs-toggle="dropdown" role="button"
-          aria-expanded="false" aria-haspopup="true"
-          id="dropdown-versions">Versions</a> <div class="dropdown-menu" aria-labelledby="dropdown-versions"> <a class="dropdown-item" data-toggle="tooltip" title="" href="https://hta-pharma.github.io/maicplus/main">main</a> </div></li>
-<!-- end dropdown for versions -->
       </ul><form class="form-inline my-2 my-lg-0" role="search">
         <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
 
diff --git a/main/reference/weighted_twt.html b/main/reference/weighted_twt.html
new file mode 100644
index 00000000..9448c180
--- /dev/null
+++ b/main/reference/weighted_twt.html
@@ -0,0 +1,163 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content='The weighted patient data for a two arm trial generated from the centered patient data (centered_ipd_twt).
+It has weights calculated for 100 bootstrap samples.
+The object is generated using the following code:
+estimate_weights(
+  data = centered_ipd_twt,
+  centered_colnames = c(
+    "AGE_CENTERED",
+    "AGE_MEDIAN_CENTERED",
+    "AGE_SQUARED_CENTERED",
+    "SEX_MALE_CENTERED",
+    "ECOG0_CENTERED",
+    "SMOKE_CENTERED"
+    ),
+  n_boot_iteration = 100
+ )
+'><title>Weighted object for two arm trial data — weighted_twt • maicplus</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.11/clipboard.min.js" integrity="sha512-7O5pXpc0oCRrxk8RUfDYFgn0nO1t+jLuIOQdOMRp4APB7uZ4vSjspzp5y6YDtDs4VzUSTbWzBFZ/LKJhnyFOKw==" crossorigin="anonymous" referrerpolicy="no-referrer"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Weighted object for two arm trial data — weighted_twt"><meta property="og:description" content='The weighted patient data for a two arm trial generated from the centered patient data (centered_ipd_twt).
+It has weights calculated for 100 bootstrap samples.
+The object is generated using the following code:
+estimate_weights(
+  data = centered_ipd_twt,
+  centered_colnames = c(
+    "AGE_CENTERED",
+    "AGE_MEDIAN_CENTERED",
+    "AGE_SQUARED_CENTERED",
+    "SEX_MALE_CENTERED",
+    "ECOG0_CENTERED",
+    "SMOKE_CENTERED"
+    ),
+  n_boot_iteration = 100
+ )
+'><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
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+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+  gtag('config', 'UA-125641273-1');
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+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+    
+
+    <nav class="navbar fixed-top navbar-dark navbar-expand-lg bg-primary" data-bs-theme="dark"><div class="container">
+    
+    <a class="navbar-brand me-2" href="../index.html">maicplus</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
+
+    
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
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+</li>
+<li class="nav-item dropdown">
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+  </div>
+</li>
+<li class="nav-item">
+  <a class="nav-link" href="../news/index.html">Changelog</a>
+</li>
+      </ul><form class="form-inline my-2 my-lg-0" role="search">
+        <input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
+
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+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="" class="logo" alt=""><h1>Weighted object for two arm trial data</h1>
+      <small class="dont-index">Source: <a href="https://github.com/hta-pharma/maicplus/blob/HEAD/R/data.R" class="external-link"><code>R/data.R</code></a></small>
+      <div class="d-none name"><code>weighted_twt.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>The weighted patient data for a two arm trial generated from the centered patient data (<a href="centered_ipd_twt.html">centered_ipd_twt</a>).
+It has weights calculated for 100 bootstrap samples.</p>
+<p>The object is generated using the following code:</p>
+<p></p><div class="sourceCode"><pre><code><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">estimate_weights</span>(</span>
+<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a>  <span class="at">data =</span> centered_ipd_twt,</span>
+<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a>  <span class="at">centered_colnames =</span> <span class="fu">c</span>(</span>
+<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a>    <span class="st">"AGE_CENTERED"</span>,</span>
+<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a>    <span class="st">"AGE_MEDIAN_CENTERED"</span>,</span>
+<span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a>    <span class="st">"AGE_SQUARED_CENTERED"</span>,</span>
+<span id="cb1-7"><a href="#cb1-7" tabindex="-1"></a>    <span class="st">"SEX_MALE_CENTERED"</span>,</span>
+<span id="cb1-8"><a href="#cb1-8" tabindex="-1"></a>    <span class="st">"ECOG0_CENTERED"</span>,</span>
+<span id="cb1-9"><a href="#cb1-9" tabindex="-1"></a>    <span class="st">"SMOKE_CENTERED"</span></span>
+<span id="cb1-10"><a href="#cb1-10" tabindex="-1"></a>    ),</span>
+<span id="cb1-11"><a href="#cb1-11" tabindex="-1"></a>  <span class="at">n_boot_iteration =</span> <span class="dv">100</span></span>
+<span id="cb1-12"><a href="#cb1-12" tabindex="-1"></a> )</span></code></pre><p></p></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">weighted_twt</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a></h2>
+    <p>A <code>maicplus_estimate_weights</code> object created by <code><a href="estimate_weights.html">estimate_weights()</a></code> containing</p><dl><dt>data</dt>
+<dd><p>patient level data with weights</p></dd>
+
+<dt>centered_colnames</dt>
+<dd><p>Columns used in MAIC</p></dd>
+
+<dt>nr_missing</dt>
+<dd><p>Number of observations with missing data</p></dd>
+
+<dt>ess</dt>
+<dd><p>Expected sample size</p></dd>
+
+<dt>opt</dt>
+<dd><p>Information from <code>optim</code> from weight calculation</p></dd>
+
+<dt>boot</dt>
+<dd><p>Parameters and bootstrap sample weights for the 100 samples</p></dd>
+
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>Other anchored datasets: 
+<code><a href="adsl_twt.html">adsl_twt</a></code>,
+<code><a href="adtte_twt.html">adtte_twt</a></code>,
+<code><a href="agd.html">agd</a></code>,
+<code><a href="centered_ipd_twt.html">centered_ipd_twt</a></code>,
+<code><a href="pseudo_ipd_twt.html">pseudo_ipd_twt</a></code></p></div>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by hta-pharma.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.9.</p>
+</div>
+
+    </footer></div>
+
+  
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+  
+
+  </body></html>
+
diff --git a/main/search.json b/main/search.json
index d89fc840..6404bfac 100644
--- a/main/search.json
+++ b/main/search.json
@@ -1 +1 @@
-[{"path":"https://hta-pharma.github.io/maicplus/main/articles/using_maicplus.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Using maicplus","text":"package describes steps required perform matching-adjusted indirect comparison (MAIC) analysis using maicplus package R endpoint interest either time--event (e.g. overall survival) binary (e.g. objective tumor response). methods described document based originally described Signorovitch et al. 2010 described National Institute Health Care Excellence (NICE) Decision Support Unit (DSU) Technical Support Document (TSD) 18. [signorovitch2010; phillippo2016a] MAIC methods often required : common comparator treatment link clinical trial new intervention clinical trials treatments given disease area. example study new intervention single arm trial control group. commonly referred unanchored MAIC. common comparator available link clinical trial new intervention clinical trial one treatment given disease area substantial differences patient demographic disease characteristics believed treatment effect modifiers. commonly referred anchored MAIC. premise MAIC methods adjust -trial differences patient demographic disease characteristics baseline. common treatment comparator ‘linked network’ unavailable, MAIC assumes differences absolute outcomes observed trial entirely explained imbalances prognostic variables treatment effect modifiers. Prognostic variables predictive disease outcomes, independent treatment received. example, older patients may increased risk death compared younger patients. Treatment effect modifiers variables influence relative effect one treatment compared another. example patients better performance status may experience larger treatment benefit worse performance status. assumption, every prognostic variable every treatment effect modifier imbalanced two studies must available. assumption generally considered difficult meet. [phillippo2016a] several ways identifying prognostic variables/treatment effect modifiers used MAIC analyses, include: Clinical expertise (available project) Published papers/previous submissions (identified disease area previously) Univariable/multivariable regression analyses identify covariates significant effect outcome Subgroup analyses clinical trials may identify interactions patient characteristics relative treatment effect","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/articles/using_maicplus.html","id":"theory-behind-maic","dir":"Articles","previous_headings":"","what":"Theory behind MAIC","title":"Using maicplus","text":"briefly go theory behind MAIC. detailed information, see Signorovitch et al. 2010. Let us define \\(t_i\\) treatment patient \\(\\) received. assume \\(t_i=0\\) patient received intervention (IPD) \\(t_i=1\\) patient received comparator treatment. causal effect treatment \\(T=0\\) vs \\(T=1\\) mean outcome \\(Y\\) can estimated \\[ \\frac{\\sum_{=1}^{n}y_{}(1-t_{})w_{}}{\\sum_{=1}^{n}(1-t_{})w_{}}-\\bar{y}_{1} \\] \\(w_i=\\frac{Pr(T_i=1\\mid x_i)}{Pr(T_i=0\\mid x_i)}\\) odds patient \\(\\) received treatment \\(T=1\\) vs \\(T=0\\) (.e. enrolls aggregate data study vs IPD study) given baseline characteristics \\(x_i\\). Thus, patients receiving \\(T=0\\) re-weighted match distribution patients receiving \\(T=1\\). Note causal effect case outcome \\(Y\\) continuous. outcome binary, \\(Y\\) proportion use link function logit give us causal effect odds ratio scale. propensity score methods, may assume \\(w_i\\) follow logistic regression form \\[ w_{}=exp(x_i^{T}\\beta) \\] However, order estimate \\(\\beta\\), use maximum likelihood approach IPD trials. Instead, use method moments. estimate \\(\\beta\\) weighted averages covariates IPD exactly matches aggregate data averages. Mathematically speaking, want estimate \\(\\beta\\) : \\[ 0=\\frac{\\sum_{=1}^{n}x_{}exp(x_i^{T}\\hat{\\beta})}{\\sum_{=1}^{n}exp(x_i^{T}\\hat{\\beta})}-\\bar{x}_{agg} \\] \\(x_i\\) contains confounders logistic regression \\(w_i\\) correctly specified, obtain consistent estimate causal effect intervention vs comparator treatment. equation equivalent \\[ 0=\\sum_{=1}^{n}(x_{}-\\bar{x}_{agg})exp(x_{}^{T}\\hat{\\beta}) \\] transform transform IPD subtracting aggregate data means (centering needed preprocessing). \\[ 0=\\sum_{=1}^{n}x_{}exp(x_{}^{T}\\hat{\\beta}) \\] Note first derivative \\[ Q(\\beta)=\\sum_{=1}^{n}exp(x_{}^{T}\\hat{\\beta}) \\] second derivative \\[ Q''(\\beta)=\\sum_{=1}^{n}x_ix_i^Texp(x_{}^{T}\\hat{\\beta}) \\] Since \\(Q''(\\beta)\\) positive-definite \\(\\beta\\), \\(Q(\\beta)\\) convex finite solution equation unique corresponds global minimum \\(Q(\\beta)\\). Thus, can use optimization methods calculate \\(\\beta\\).","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/articles/using_maicplus.html","id":"example-scenario","dir":"Articles","previous_headings":"","what":"Example scenario","title":"Using maicplus","text":"present unanchored MAIC two treatments lung cancer. two endpoints compared overall survival (time event outcome) objective response (binary outcome). data available : Individual patient data single arm study Aggregate summary data comparator study Pseudo patient data comparator study. required matching process needed derive relative treatment effects internal treatment comparator treatment.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/articles/using_maicplus.html","id":"preprocessing","dir":"Articles","previous_headings":"","what":"Preprocessing","title":"Using maicplus","text":"Additional suggested packages vignette:","code":"library(maicplus) library(dplyr) # this is used for data merging/cleaning. Package itself does not depend on dplyr #>  #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #>  #>     filter, lag #> The following objects are masked from 'package:base': #>  #>     intersect, setdiff, setequal, union # library(clubSandwich) # For robust standard error in logistic regression library(sandwich)  library(survival) library(survminer) # for ggsurvplot #> Loading required package: ggplot2 #> Loading required package: ggpubr #>  #> Attaching package: 'survminer' #> The following object is masked from 'package:survival': #>  #>     myeloma library(ggplot2) # for ggplot functions library(boot) # for bootstrapping #>  #> Attaching package: 'boot' #> The following object is masked from 'package:survival': #>  #>     aml"},{"path":"https://hta-pharma.github.io/maicplus/main/articles/using_maicplus.html","id":"preprocessing-ipd","dir":"Articles","previous_headings":"Preprocessing","what":"Preprocessing IPD","title":"Using maicplus","text":"example scenario, age, sex, Eastern Cooperative Oncology Group (ECOG) performance status, smoking status, number previous treatments identified imbalanced prognostic variables/treatment effect modifiers. example reads combines data three standard simulated data sets (adsl, adrs adtte) saved ‘.csv’ files.","code":"adsl <- read.csv(system.file(\"extdata\", \"adsl.csv\",   package = \"maicplus\",   mustWork = TRUE )) adrs <- read.csv(system.file(\"extdata\", \"adrs.csv\",   package = \"maicplus\",   mustWork = TRUE )) adtte <- read.csv(system.file(\"extdata\", \"adtte.csv\", package = \"maicplus\", mustWork = TRUE))  # Data containing the matching variables adsl <- adsl %>%   mutate(SEX_MALE = ifelse(SEX == \"Male\", 1, 0)) %>%   mutate(AGE_SQUARED = AGE^2)  # Could use built-in function for dummizing variables # adsl <- dummize_ipd(adsl, dummize_cols=c(\"SEX\"), dummize_ref_level=c(\"Female\"))  # Response data adrs <- adrs %>%   filter(PARAM == \"Response\") %>%   transmute(USUBJID, ARM, RESPONSE = AVAL)  # Time to event data (overall survival) adtte <- adtte %>%   filter(PARAMCD == \"OS\") %>%   mutate(EVENT = 1 - CNSR) %>%   transmute(USUBJID, ARM, TIME = AVAL, EVENT)  # Combine all ipd data ipd <- adsl %>%   full_join(adrs, by = c(\"USUBJID\", \"ARM\")) %>%   full_join(adtte, by = c(\"USUBJID\", \"ARM\")) head(ipd) #>   X USUBJID ARM AGE    SEX SMOKE ECOG0 N_PR_THER SEX_MALE AGE_SQUARED RESPONSE #> 1 1       1   A  45   Male     0     0         4        1        2025        0 #> 2 2       2   A  71   Male     0     0         3        1        5041        1 #> 3 3       3   A  58   Male     1     1         2        1        3364        1 #> 4 4       4   A  48 Female     0     1         4        0        2304        1 #> 5 5       5   A  69   Male     0     1         4        1        4761        0 #> 6 6       6   A  48 Female     0     1         4        0        2304        0 #>       TIME EVENT #> 1 281.5195     0 #> 2 500.0000     0 #> 3 304.6406     0 #> 4 102.4731     0 #> 5 101.6632     0 #> 6 237.0593     1"},{"path":"https://hta-pharma.github.io/maicplus/main/articles/using_maicplus.html","id":"preprocessing-aggregate-data","dir":"Articles","previous_headings":"Preprocessing","what":"Preprocessing aggregate data","title":"Using maicplus","text":"two ways specifying aggregate data. One approach read aggregate data using excel spreadsheet. spreadsheet, possible variable types include mean, median, standard deviation continuous variables count proportion binary variables. naming followed suffixes accordingly: _COUNT, _MEAN, _MEDIAN, _SD, _PROP. , process_agd convert count proportions. way define data frame aggregate data R. way, _COUNT prefix specified proportion allowed binary variables. suffix names first approach. Possible missingness binary variables accounted subtracting denominator missing count .e. proportion = count / (N - missing).","code":"# Through excel spreadsheet # target_pop <- read.csv(system.file(\"extdata\",\"aggregate_data_example_1.csv\", package = \"maicplus\", mustWork = TRUE)) # agd <- process_agd(target_pop)  # Second approach by defining a data frame in R agd <- data.frame(   STUDY = \"Lung study\",   ARM = \"Total\",   N = 300,   AGE_MEAN = 51,   AGE_MEDIAN = 49,   AGE_SD = 3.25,   SEX_MALE_PROP = 147 / 300,   ECOG0_PROP = 0.40,   SMOKE_PROP = 58 / (300 - 5),   N_PR_THER_MEDIAN = 2 )"},{"path":"https://hta-pharma.github.io/maicplus/main/articles/using_maicplus.html","id":"preprocessing-aggregate-data-1","dir":"Articles","previous_headings":"Preprocessing","what":"Preprocessing aggregate data","title":"Using maicplus","text":"","code":"#### prepare data ipd_centered <- center_ipd(ipd = ipd, agd = agd) head(ipd_centered) #>   X USUBJID ARM AGE    SEX SMOKE ECOG0 N_PR_THER SEX_MALE AGE_SQUARED RESPONSE #> 1 1       1   A  45   Male     0     0         4        1        2025        0 #> 2 2       2   A  71   Male     0     0         3        1        5041        1 #> 3 3       3   A  58   Male     1     1         2        1        3364        1 #> 4 4       4   A  48 Female     0     1         4        0        2304        1 #> 5 5       5   A  69   Male     0     1         4        1        4761        0 #> 6 6       6   A  48 Female     0     1         4        0        2304        0 #>       TIME EVENT AGE_CENTERED AGE_MEDIAN_CENTERED AGE_SQUARED_CENTERED #> 1 281.5195     0           -6                -0.5            -586.5625 #> 2 500.0000     0           20                 0.5            2429.4375 #> 3 304.6406     0            7                 0.5             752.4375 #> 4 102.4731     0           -3                -0.5            -307.5625 #> 5 101.6632     0           18                 0.5            2149.4375 #> 6 237.0593     1           -3                -0.5            -307.5625 #>   SEX_MALE_CENTERED ECOG0_CENTERED SMOKE_CENTERED N_PR_THER_MEDIAN_CENTERED #> 1              0.51           -0.4     -0.1966102                       0.5 #> 2              0.51           -0.4     -0.1966102                       0.5 #> 3              0.51            0.6      0.8033898                      -0.5 #> 4             -0.49            0.6     -0.1966102                       0.5 #> 5              0.51            0.6     -0.1966102                       0.5 #> 6             -0.49            0.6     -0.1966102                       0.5"},{"path":"https://hta-pharma.github.io/maicplus/main/articles/using_maicplus.html","id":"how-to-handle-standard-deviation-aggregate-summary","dir":"Articles","previous_headings":"Preprocessing > Preprocessing aggregate data","what":"How to handle standard deviation aggregate summary","title":"Using maicplus","text":"described Phillippo et al. 2016, balancing mean standard deviation continuous variables (possible) may considered cases. standard deviation provided comparator population, preprocessing done target population, \\(E(X^2)\\) calculated using variance formula \\(Var(X)=E(X^{2})-E(X)^{2}\\). \\(E(X^2)\\) target population matched IPD level data, \\(X^{2}\\) calculated preprocessing stage IPD.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/articles/using_maicplus.html","id":"how-to-handle-median-aggregate-summary","dir":"Articles","previous_headings":"Preprocessing > Preprocessing aggregate data","what":"How to handle median aggregate summary","title":"Using maicplus","text":"median provided, IPD preprocessed categorize variable binary variable. values IPD higher comparator population median assigned value 1. Conversely, values lower assigned value 0. Comparator population median replaced 0.5 adjust categorization IPD data. newly created IPD binary variable matched proportion 0.5.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/articles/using_maicplus.html","id":"calculating-weights","dir":"Articles","previous_headings":"","what":"Calculating weights","title":"Using maicplus","text":"Another check weights calculated look weighted covariates match aggregate data summary.","code":"# list variables that are going to be used to match centered_colnames <- c(\"AGE\", \"AGE_SQUARED\", \"SEX_MALE\", \"ECOG0\", \"SMOKE\", \"N_PR_THER_MEDIAN\") centered_colnames <- paste0(centered_colnames, \"_CENTERED\")  weighted_data <- estimate_weights(   data = ipd_centered,   centered_colnames = centered_colnames )  # Alternatively, you can specify the numeric column locations for centered_colnames # weighted_data <- estimate_weights(ipd_centered, centered_colnames = c(14, 16:20))  # Two options to plot weights plot using base R or ggplot plot(weighted_data) plot(weighted_data, ggplot = TRUE) outdata <- check_weights(weighted_data, agd) outdata #>   covariate match_stat internal_trial internal_trial_after_weighted #> 1       AGE       Mean         59.850                         51.00 #> 2       AGE         SD          9.011                          3.25 #> 3  SEX_MALE       Prop          0.380                          0.49 #> 4     ECOG0       Prop          0.410                          0.40 #> 5     SMOKE       Prop          0.320                          0.20 #> 6 N_PR_THER     Median          3.000                          2.00 #>   external_trial sum_centered_IPD_with_weights #> 1          51.00                        0.0001 #> 2           3.25                        0.0125 #> 3           0.49                        0.0000 #> 4           0.40                        0.0000 #> 5           0.20                        0.0000 #> 6           2.00                        0.0000"},{"path":"https://hta-pharma.github.io/maicplus/main/articles/using_maicplus.html","id":"time-to-event-analysis","dir":"Articles","previous_headings":"","what":"Time to event analysis","title":"Using maicplus","text":"first need combine internal IPD data pseudo comparator IPD. obtain pseudo comparator IPD, digitize Kaplan Meier curves comparator study.","code":"pseudo_ipd <- read.csv(system.file(\"extdata\", \"psuedo_IPD.csv\", package = \"maicplus\", mustWork = TRUE)) ipd_matched <- weighted_data$data  # Need to specify pseudo_ipd ARM if not specified pseudo_ipd$ARM <- \"B\" # Need to specify ARM for pseudo ipd  # Need to specify weights for pseudo_ipd which is 1 pseudo_ipd$weights <- 1  # make sure pseudo_ipd name has same name compared with ipd_matched for time, event, arm colnames(pseudo_ipd) <- c(\"TIME\", \"EVENT\", \"ARM\", \"weights\")  combined_data_tte <- rbind(   ipd_matched[, colnames(pseudo_ipd)],   pseudo_ipd )  head(combined_data_tte) #>       TIME EVENT ARM      weights #> 1 281.5195     0   A 6.884028e-01 #> 2 500.0000     0   A 5.669105e-08 #> 3 304.6406     0   A 1.146355e-01 #> 4 102.4731     0   A 1.178307e+00 #> 5 101.6632     0   A 1.605203e-06 #> 6 237.0593     1   A 1.178307e+00"},{"path":"https://hta-pharma.github.io/maicplus/main/articles/using_maicplus.html","id":"report-1-kaplan-meier-plot","dir":"Articles","previous_headings":"Time to event analysis","what":"Report 1: Kaplan-Meier plot","title":"Using maicplus","text":"also \"ggplot\" option Kaplan-Meier curves using survminer R package.","code":"kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data_tte, conf.type = \"log-log\") kmobj_adj <- survfit(Surv(TIME, EVENT) ~ ARM,   combined_data_tte,   weights = combined_data_tte$weights, conf.type = \"log-log\" )  par(cex.main = 0.85)  kmdat <- do.call(rbind, survfit_makeup(kmobj)) kmdat$treatment <- factor(kmdat$treatment) basic_kmplot(kmdat,   time_scale = \"month\",   time_grid = seq(0, 20, by = 2),   show_risk_set = FALSE,   main_title = \"Kaplan-Meier Curves\",   subplot_heights = NULL,   suppress_plot_layout = FALSE,   use_colors = NULL,   use_line_types = NULL ) # km_plot2(combined_data_tte, trt = \"A\", trt_ext = \"B\", censor = TRUE, risk.table = TRUE)"},{"path":"https://hta-pharma.github.io/maicplus/main/articles/using_maicplus.html","id":"report-2-analysis-table-cox-model-before-and-after-matching-incl-median-survival-time","dir":"Articles","previous_headings":"Time to event analysis","what":"Report 2: Analysis table (Cox model) before and after matching, incl Median Survival Time","title":"Using maicplus","text":"can fit cox regression model using combined dataset. weight adjusted cox regression, fit model robust standard errors. Along hazard ratios, can also find median survival time using medSurv_makeup function. , report_table_tte function nicely combines information together create result table.","code":"# Fit a Cox model with/without weights to estimate the HR unweighted_cox <- coxph(Surv(TIME, EVENT == 1) ~ ARM, data = combined_data_tte) weighted_cox <- coxph(Surv(TIME, EVENT == 1) ~ ARM,   data = combined_data_tte, weights = combined_data_tte$weights, robust = TRUE )  # Derive median survival time medSurv <- medSurv_makeup(kmobj, legend = \"before matching\", time_scale = \"day\") medSurv_adj <- medSurv_makeup(kmobj_adj, legend = \"after matching\", time_scale = \"day\") medSurv_out <- rbind(medSurv, medSurv_adj) medSurv_out #>   treatment            type records    n.max  n.start    events    rmean #> 1     ARM=A before matching     500 500.0000 500.0000 190.00000 265.1012 #> 2     ARM=B before matching     300 300.0000 300.0000 178.00000 130.9893 #> 3     ARM=A  after matching     500 199.8422 199.8422  66.84953 307.7223 #> 4     ARM=B  after matching     300 300.0000 300.0000 178.00000 130.9893 #>   se(rmean)    median   0.95LCL  0.95UCL #> 1  10.80981 230.94839 191.10767 313.1574 #> 2  10.24910  83.58535  68.82298 101.0786 #> 3  16.71338 362.20670 237.05932 452.7209 #> 4  10.24910  83.58535  68.82298 101.0786 rbind(   report_table_tte(unweighted_cox, medSurv, tag = paste0(\"Before/\", \"Overall survival\")),   report_table_tte(weighted_cox, medSurv_adj, tag = paste0(\"After/\", \"Overall survival\")) ) #>                   Matching treatment     N n.events(%)     median[95% CI] #> 2  Before/Overall survival     ARM=B 300.0   178(59.3)  83.6[ 68.8;101.1] #> 1                              ARM=A 500.0   190(38.0) 230.9[191.1;313.2] #> 21  After/Overall survival     ARM=B 300.0   178(59.3)  83.6[ 68.8;101.1] #> 11                             ARM=A 199.8  66.8(33.5) 362.2[237.1;452.7] #>         HR[95% CI] p-Value #> 2  2.67[2.16;3.29]  <0.001 #> 1                          #> 21 3.46[2.53;4.74]  <0.001 #> 11"},{"path":"https://hta-pharma.github.io/maicplus/main/articles/using_maicplus.html","id":"report-3-bootstrap-result","dir":"Articles","previous_headings":"Time to event analysis","what":"Report 3: Bootstrap result","title":"Using maicplus","text":"","code":"set.seed(1) HR_bootstraps <- boot(   data = ipd_centered,   statistic = bootstrap_HR,   centered_colnames = centered_colnames,   pseudo_ipd = pseudo_ipd,   model = Surv(TIME, EVENT == 1) ~ ARM,   ref_treat = \"B\",   R = 1000 ) # Median of the bootstrap samples HR_median <- median(HR_bootstraps$t)  # Bootstrap CI - Percentile CI boot_ci_HR <- boot.ci(boot.out = HR_bootstraps, index = 1, type = \"perc\")  # Bootstrap CI - BCa CI boot_ci_HR_BCA <- boot.ci(boot.out = HR_bootstraps, index = 1, type = \"bca\")  HR_median #> [1] 0.2858165 boot_ci_HR #> BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS #> Based on 1000 bootstrap replicates #>  #> CALL :  #> boot.ci(boot.out = HR_bootstraps, type = \"perc\", index = 1) #>  #> Intervals :  #> Level     Percentile      #> 95%   ( 0.2236,  0.3689 )   #> Calculations and Intervals on Original Scale boot_ci_HR_BCA #> BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS #> Based on 1000 bootstrap replicates #>  #> CALL :  #> boot.ci(boot.out = HR_bootstraps, type = \"bca\", index = 1) #>  #> Intervals :  #> Level       BCa           #> 95%   ( 0.2296,  0.3789 )   #> Calculations and Intervals on Original Scale"},{"path":"https://hta-pharma.github.io/maicplus/main/articles/using_maicplus.html","id":"report-4-diagnosis-plot","dir":"Articles","previous_headings":"Time to event analysis","what":"Report 4: Diagnosis Plot","title":"Using maicplus","text":"","code":"# grambsch & theaneu ph test coxdiag <- cox.zph(unweighted_cox, global = FALSE, transform = \"log\") coxdiag_adj <- cox.zph(weighted_cox, global = FALSE, transform = \"log\")  coxdiag #>       chisq df    p #> ARM 0.00996  1 0.92 par(mfrow = c(1, 1), tcl = -0.15) plot(coxdiag, yaxt = \"n\", main = \"Grambsch & Terneau Plot (before matching)\") axis(2, las = 1) coxdiag_adj #>      chisq df    p #> ARM 0.0438  1 0.83 par(mfrow = c(1, 1), tcl = -0.15) plot(coxdiag_adj, yaxt = \"n\", main = \"Grambsch & Terneau Plot (after matching)\") axis(2, las = 1) # log-cumulative hazard plot ph_diagplot_lch(kmobj,   time_scale = \"month\", log_time = TRUE,   endpoint_name = \"OS\", subtitle = \"(Before Matching)\" ) ph_diagplot_lch(kmobj_adj,   time_scale = \"month\", log_time = TRUE,   endpoint_name = \"OS\", subtitle = \"(After Matching)\" ) # schoenfeld residual plot ph_diagplot_schoenfeld(unweighted_cox,   time_scale = \"month\", log_time = TRUE,   endpoint_name = \"OS\", subtitle = \"(Before Matching)\" ) ph_diagplot_schoenfeld(weighted_cox,   time_scale = \"month\", log_time = TRUE,   endpoint_name = \"OS\", subtitle = \"(After Matching)\" )"},{"path":"https://hta-pharma.github.io/maicplus/main/articles/using_maicplus.html","id":"analysis-using-a-built-in-wrapper","dir":"Articles","previous_headings":"Time to event analysis","what":"Analysis using a built-in wrapper","title":"Using maicplus","text":"One can analysis wrapper","code":"# put in wrapper code"},{"path":"https://hta-pharma.github.io/maicplus/main/articles/using_maicplus.html","id":"binary-outcome-analysis-todo","dir":"Articles","previous_headings":"","what":"Binary outcome analysis (TODO)","title":"Using maicplus","text":"","code":"# Simulate response data based on the known proportion of responders comparator_prop_events <- 0.4  # Calculate number with event. Use round() to ensure we end up with a whole number of people. # number without an event = Total N - number with event to ensure we keep the same number of patients n_with_event <- round(agd$N * comparator_prop_events, digits = 0) comparator_binary <- data.frame(\"RESPONSE\" = c(rep(1, n_with_event), rep(0, agd$N - n_with_event))) comparator_binary$ARM <- \"B\" # need to specify ARM for comparator data ipd_matched <- weighted_data$data  combined_data_binary <- merge_two_data(comparator_binary, ipd_matched, internal_response_name = \"RESPONSE\")  unweighted_OR <- glm(   formula = RESPONSE ~ ARM,   family = binomial(link = \"logit\"),   data = combined_data_binary )  # Log odds ratio log_OR_CI <- cbind(coef(unweighted_OR), confint.default(unweighted_OR, level = 0.95))[2, ]  # Odds ratio OR_CI <- exp(log_OR_CI) names(OR_CI) <- c(\"OR\", \"OR_low_CI\", \"OR_upp_CI\") OR_CI  # Fit a logistic regression model with weights to estimate the weighted OR weighted_OR <- suppressWarnings(glm(   formula = RESPONSE ~ ARM,   family = binomial(link = \"logit\"),   data = combined_data_binary,   weight = weights ))  # Weighted log odds ratio log_OR_CI_wtd <- cbind(coef(weighted_OR), confint.default(weighted_OR, level = 0.95))[2, ]  # Weighted odds ratio OR_CI_wtd <- exp(log_OR_CI_wtd) names(OR_CI_wtd) <- c(\"OR\", \"OR_low_CI\", \"OR_upp_CI\") OR_CI_wtd  # Robust standard error if (requireNamespace(\"clubSandwich\")) {   vmod <- clubSandwich::vcovCR(weighted_OR, cluster = seq_len(dim(combined_data_binary)[1]), type = \"CR2\")   coef_res <- clubSandwich::conf_int(weighted_OR, vmod, coef = 2)    OR_CI_robust <- exp(with(coef_res, c(beta, CI_L, CI_U)))   names(OR_CI_robust) <- c(\"Estimate\", \"Lower 95% CI\", \"Upper 95% CI\")   OR_CI_robust } # Using sandwich package V.sw <- sandwich::vcovHC(weighted_OR) # white's estimator SD <- sqrt(V.sw[2, 2]) Estimate <- coef(weighted_OR)[2] OR_CI_robust2 <- exp(c(Estimate, Estimate - 1.96 * SD, Estimate + 1.96 * SD)) names(OR_CI_robust2) <- c(\"Estimate\", \"Lower 95% CI\", \"Upper 95% CI\") OR_CI_robust2"},{"path":"https://hta-pharma.github.io/maicplus/main/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"hta-pharma. Author, maintainer.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"hta-pharma (2024). maicplus: Matching Adjusted Indirect Comparison. R package version 0.1.0, https://github.com/hta-pharma/maicplus/.","code":"@Manual{,   title = {maicplus: Matching Adjusted Indirect Comparison},   author = {{hta-pharma}},   year = {2024},   note = {R package version 0.1.0},   url = {https://github.com/hta-pharma/maicplus/}, }"},{"path":"https://hta-pharma.github.io/maicplus/main/index.html","id":"maicplus","dir":"","previous_headings":"","what":"Matching Adjusted Indirect Comparison","title":"Matching Adjusted Indirect Comparison","text":"open source R package MAIC","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adrs_sat.html","id":null,"dir":"Reference","previous_headings":"","what":"Binary outcome data from single arm trial — adrs_sat","title":"Binary outcome data from single arm trial — adrs_sat","text":"Binary outcome data single arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adrs_sat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Binary outcome data from single arm trial — adrs_sat","text":"","code":"adrs_sat"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adrs_sat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Binary outcome data from single arm trial — adrs_sat","text":"data frame 500 rows 5 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm. AVAL Analysis value, dataset indicator response. PARAM Parameter type AVAL. RESPONSE Indicator response.","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adrs_twt.html","id":null,"dir":"Reference","previous_headings":"","what":"Binary outcome data from two arm trial — adrs_twt","title":"Binary outcome data from two arm trial — adrs_twt","text":"Binary outcome data two arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adrs_twt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Binary outcome data from two arm trial — adrs_twt","text":"","code":"adrs_twt"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adrs_twt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Binary outcome data from two arm trial — adrs_twt","text":"data frame 1000 rows 5 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm, \"\", \"C\". AVAL Analysis value, dataset indicator response. PARAM Parameter type AVAL. RESPONSE Indicator response.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adsl_sat.html","id":null,"dir":"Reference","previous_headings":"","what":"Patient data from single arm study — adsl_sat","title":"Patient data from single arm study — adsl_sat","text":"Patient data single arm study","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adsl_sat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Patient data from single arm study — adsl_sat","text":"","code":"adsl_sat"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adsl_sat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Patient data from single arm study — adsl_sat","text":"data frame 500 rows 8 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm. AGE Age years baseline. SEX Sex patient recorded character \"Male\"/\"Female\". SMOKE Smoking status baseline integer 1/0. ECOG0 Indicator ECOG score = 0 baseline integer 1/0. N_PR_THER Number prior therapies received integer 1, 2, 3, 4. SEX_MALE Indicator SEX == \"Male\" numeric 1/0.","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adsl_twt.html","id":null,"dir":"Reference","previous_headings":"","what":"Patient data from two arm trial — adsl_twt","title":"Patient data from two arm trial — adsl_twt","text":"Patient data two arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adsl_twt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Patient data from two arm trial — adsl_twt","text":"","code":"adsl_twt"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adsl_twt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Patient data from two arm trial — adsl_twt","text":"data frame 1000 rows 8 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm. AGE Age years baseline. SEX Sex patient recorded character \"Male\"/\"Female\" SMOKE Smoking status baseline integer 1/0. ECOG0 Indicator ECOG score = 0 baseline integer 1/0. N_PR_THER Number prior therapies received integer 1, 2, 3, 4. SEX_MALE Indicator SEX == \"Male\" numeric 1/0","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adtte_sat.html","id":null,"dir":"Reference","previous_headings":"","what":"Survival data from single arm trial — adtte_sat","title":"Survival data from single arm trial — adtte_sat","text":"Survival data single arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adtte_sat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Survival data from single arm trial — adtte_sat","text":"","code":"adtte_sat"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adtte_sat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Survival data from single arm trial — adtte_sat","text":"data frame 500 rows 10 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm, \"\". AVAL Analysis value dataset overall survival time days. AVALU Unit AVAL. PARAMCD Paramater code AVAL, \"OS\". PARAM Parameter name AVAL, \"Overall Survival. CNSR Censoring indicator 0/1. TIME Survival time days. EVENT Event indicator 0/1.","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adtte_twt.html","id":null,"dir":"Reference","previous_headings":"","what":"Survival data from two arm trial — adtte_twt","title":"Survival data from two arm trial — adtte_twt","text":"Survival data two arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adtte_twt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Survival data from two arm trial — adtte_twt","text":"","code":"adtte_twt"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/adtte_twt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Survival data from two arm trial — adtte_twt","text":"data frame 1000 rows 10 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm, \"\", \"C\". AVAL Analysis value dataset overall survival time days. AVALU Unit AVAL. PARAMCD Parameter code AVAL, \"OS\". PARAM Parameter name AVAL, \"Overall Survival. CNSR Censoring indicator 0/1. TIME Survival time days. EVENT Event indicator 0/1.","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/agd.html","id":null,"dir":"Reference","previous_headings":"","what":"Aggregate effect modifier data from published study — agd","title":"Aggregate effect modifier data from published study — agd","text":"data formatted used center_ipd().","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/agd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aggregate effect modifier data from published study — agd","text":"","code":"agd"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/agd.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Aggregate effect modifier data from published study — agd","text":"data frame 3 rows 9 columns: STUDY study name, Study_XXXX ARM Study arm name total N Number observations study arm AGE_MEAN Mean age study arm AGE_MEDIAN Median age study arm AGE_SD Standard deviation age study arm SEX_MALE_COUNT Number male patients ECOG0_COUNT Number patients ECOG score = 0 SMOKE_COUNT Number smokers N_PR_THER_MEDIAN Median number prior therapies","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/basic_kmplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Basic Kaplan Meier (KM) plot function — basic_kmplot","title":"Basic Kaplan Meier (KM) plot function — basic_kmplot","text":"function can generate basic KM plot without risk set table appended bottom. single plot, can include 4 KM curves. depends number levels 'treatment' column input data.frame kmdat","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/basic_kmplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Basic Kaplan Meier (KM) plot function — basic_kmplot","text":"","code":"basic_kmplot(   kmdat,   endpoint_name = \"Time to Event Endpoint\",   time_scale,   time_grid,   show_risk_set = TRUE,   main_title = \"Kaplan-Meier Curves\",   subplot_heights = NULL,   suppress_plot_layout = FALSE,   use_colors = NULL,   use_line_types = NULL,   use_pch_cex = 0.65,   use_pch_alpha = 100 )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/basic_kmplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Basic Kaplan Meier (KM) plot function — basic_kmplot","text":"kmdat data.frame, must consist treatment, time (unit days), n.risk, censor, surv, similar output maicplus:::survfit_makeup endpoint_name string, name time event endpoint, show last line title time_scale string, time unit median survival time, taking value 'years', 'months', 'weeks' 'days' time_grid numeric vector unit time_scale, risk set table x axis km plot defined based time grid show_risk_set logical, show risk set table , TRUE default main_title string, main title KM plot subplot_heights numeric vector, heights argument graphic::layout(),NULL default means user use default setting suppress_plot_layout logical, suppress layout setting function user can specify layout outside function, FALSE default use_colors character vector length 4, colors KM curves, passed col lines() use_line_types numeric vector length 4, line type KM curves, passed lty lines() use_pch_cex scalar 0 1, point size indicate censored individuals KM curves, passed cex points() use_pch_alpha scalar 0 255, degree color transparency points indicate censored individuals KM curves, passed cex points()","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/basic_kmplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Basic Kaplan Meier (KM) plot function — basic_kmplot","text":"KM plot without risk set table appended bottom, 4 KM curves","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/basic_kmplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Basic Kaplan Meier (KM) plot function — basic_kmplot","text":"","code":"library(survival) load(system.file(\"extdata\", \"combined_data_tte.rda\", package = \"maicplus\", mustWork = TRUE)) kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data_tte, conf.type = \"log-log\") kmdat <- do.call(rbind, survfit_makeup(kmobj)) kmdat$treatment <- factor(kmdat$treatment)  # without risk set table basic_kmplot(kmdat,   time_scale = \"month\",   time_grid = seq(0, 20, by = 2),   show_risk_set = FALSE,   main_title = \"Kaplan-Meier Curves\",   subplot_heights = NULL,   suppress_plot_layout = FALSE,   use_colors = NULL,   use_line_types = NULL )   # with risk set table basic_kmplot(kmdat,   time_scale = \"month\",   time_grid = seq(0, 20, by = 2),   show_risk_set = TRUE,   main_title = \"Kaplan-Meier Curves\",   subplot_heights = NULL,   suppress_plot_layout = FALSE,   use_colors = NULL,   use_line_types = NULL )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/basic_kmplot2.html","id":null,"dir":"Reference","previous_headings":"","what":"Basic Kaplan Meier (KM) plot function using ggplot — basic_kmplot2","title":"Basic Kaplan Meier (KM) plot function using ggplot — basic_kmplot2","text":"function generates basic KM plot using ggplot.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/basic_kmplot2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Basic Kaplan Meier (KM) plot function using ggplot — basic_kmplot2","text":"","code":"basic_kmplot2(   kmlist,   kmlist_name,   endpoint_name = \"Time to Event Endpoint\",   show_risk_set = TRUE,   main_title = \"Kaplan-Meier Curves\",   break_x_by = NULL,   censor = TRUE,   xlim = NULL,   use_colors = NULL,   use_line_types = NULL )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/basic_kmplot2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Basic Kaplan Meier (KM) plot function using ggplot — basic_kmplot2","text":"kmlist list survfit object kmlist_name vector indicating treatment names survfit object endpoint_name string, name time event endpoint, show last line title show_risk_set logical, show risk set table , TRUE default main_title string, main title KM plot break_x_by bin parameter survminer censor indicator include censor information xlim x limit x-axis plot use_colors character vector length 4, colors KM curves, passed 'col' lines() use_line_types numeric vector length 4, line type KM curves, passed lty lines()","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/bootstrap_HR.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrapping weighted hazard ratios to obtain confidence interval — bootstrap_HR","title":"Bootstrapping weighted hazard ratios to obtain confidence interval — bootstrap_HR","text":"function uses boot resample internal IPD data obtain bootstrapped hazard ratio values used form confidence interval. Note due lack full IPD covariates aggregate data, pseudo IPD bootstrapped.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/bootstrap_HR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrapping weighted hazard ratios to obtain confidence interval — bootstrap_HR","text":"","code":"bootstrap_HR(   ipd_centered,   i,   centered_colnames,   pseudo_ipd = NULL,   model,   min_weight = 1e-04,   ref_treat = NULL )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/bootstrap_HR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrapping weighted hazard ratios to obtain confidence interval — bootstrap_HR","text":"ipd_centered data frame containing individual patient data internal IPD study. data frame already centered using center_ipd function Index used select sample within boot. centered_colnames character vector giving names covariates use matching. names must match column names internal dataset. pseudo_ipd data frame containing pseudo individual patient data comparator study needed derive relative treatment effect. Pseudo IPD used unanchored case. pseudo_ipd time, event, ARM variable name order ipd_centered model Survival model run calculate HR correct time, event, #'   ARM names min_weight numeric value defines minimum weight allowed. value (default 0.0001) replace weights estimated 0 sample. ref_treat Reference treatment survival model","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/bootstrap_HR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrapping weighted hazard ratios to obtain confidence interval — bootstrap_HR","text":"HR bootstraps","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/bootstrap_HR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrapping weighted hazard ratios to obtain confidence interval — bootstrap_HR","text":"","code":"# Unanchored example load(system.file(\"extdata\", \"ipd.rda\", package = \"maicplus\", mustWork = TRUE)) load(system.file(\"extdata\", \"agd.rda\", package = \"maicplus\", mustWork = TRUE)) ipd_centered <- center_ipd(ipd = ipd, agd = agd)  pseudo_ipd <- read.csv(   system.file(\"extdata\", \"psuedo_IPD.csv\", package = \"maicplus\", mustWork = TRUE) ) pseudo_ipd$ARM <- \"B\" # Need to specify ARM for pseudo ipd  # Need to ensure pseudo_ipd and ipd_matched have same names colnames(pseudo_ipd) <- c(\"TIME\", \"EVENT\", \"ARM\") if (FALSE) { HR_bootstraps <- boot(   data = ipd_centered,   statistic = bootstrap_HR,   centered_colnames = centered_colnames,   pseudo_ipd = pseudo_ipd,   model = Surv(TIME, EVENT == 1) ~ ARM,   ref_treat = \"B\",   R = 1000 ) }"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/bucher.html","id":null,"dir":"Reference","previous_headings":"","what":"Bucher method for combining treatment effects — bucher","title":"Bucher method for combining treatment effects — bucher","text":"Given two treatment effects vs. C B vs. C derive treatment effects vs. B using Bucher method. Two-sided confidence interval Z-test p-value also calculated. Treatment effects standard errors log scale hazard ratio, odds ratio, risk ratio. Treatment effects standard errors natural scale risk difference mean difference.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/bucher.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bucher method for combining treatment effects — bucher","text":"","code":"bucher(trt, com, conf_lv = 0.95)  # S3 method for maicplus_bucher print(x, ci_digits = 2, pval_digits = 3, exponentiate = FALSE, ...)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/bucher.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bucher method for combining treatment effects — bucher","text":"trt list two scalars study experimental arm. 'est' point estimate 'se' standard error treatment effect. time--event data, 'est' 'se' point estimate standard error log hazard ratio. binary data, 'est' 'se' point estimate standard error log odds ratio, log risk ratio, risk difference. continuous data,  'est' 'se' point estimate standard error mean difference. com trt, study control arm conf_lv numerical scalar, prescribe confidence level derive two-sided confidence interval treatment effect x maicplus_bucher object ci_digits integer, number decimal places point estimate derived confidence limits pval_digits integer, number decimal places display Z-test p-value exponentiate whether treatment effect confidence interval exponentiated. applies relative treatment effects. Default set false. ... used","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/bucher.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bucher method for combining treatment effects — bucher","text":"list 5 elements, est scalar, point estimate treatment effect se scalar, standard error treatment effect ci_l scalar, lower confidence limit two-sided CI prescribed nominal level conf_lv ci_u scalar, upper confidence limit two-sided CI prescribed nominal level conf_lv pval p-value Z-test, null hypothesis est zero","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/bucher.html","id":"methods-by-generic-","dir":"Reference","previous_headings":"","what":"Methods (by generic)","title":"Bucher method for combining treatment effects — bucher","text":"print(maicplus_bucher): Print method maicplus_bucher objects","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/bucher.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bucher method for combining treatment effects — bucher","text":"","code":"trt <- list(est = log(1.1), se = 0.2) com <- list(est = log(1.3), se = 0.18) result <- bucher(trt, com, conf_lv = 0.9) print(result, ci_digits = 3, pval_digits = 3) #>                   result                   pvalue  #> \"-0.167 [-0.610; 0.276]\"                  \"0.535\""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/calculate_weights_legend.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","title":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","text":"Calculates ESS reduction median weights used create legend weights plot","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/calculate_weights_legend.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","text":"","code":"calculate_weights_legend(weighted_data)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/calculate_weights_legend.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","text":"weighted_data object returned calculating weights using estimate_weights","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/calculate_weights_legend.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","text":"list ESS, ESS reduction, median value scaled unscaled weights, missing count","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/calculate_weights_legend.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","text":"","code":"data(\"weighted_sat\") calculate_weights_legend(weighted_sat) #> $ess #> [1] 121.99 #>  #> $ess_reduction #> [1] 75.6 #>  #> $wt_median #> [1] 0.0567 #>  #> $wt_scaled_median #> [1] 0.1635 #>  #> $nr_na #> [1] 0 #>"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/center_ipd.html","id":null,"dir":"Reference","previous_headings":"","what":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","title":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","text":"function subtracts IPD variables (prognostic variables /effect modifiers) aggregate data averages. centering needed order calculate weights. IPD aggregate data variable names match.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/center_ipd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","text":"","code":"center_ipd(ipd, agd)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/center_ipd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","text":"ipd IPD variable names match aggregate data names without suffix. involve either changing aggregate data name ipd name. instance, binarize SEX variable MALE reference using dummize_ipd, function names new variable SEX_MALE. case, SEX_MALE also available aggregate data. agd pre-processed aggregate data contain STUDY, ARM, N. Variable names followed legal suffixes (.e. MEAN, MEDIAN, SD, PROP). Note COUNT suffix longer accepted.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/center_ipd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","text":"centered ipd using aggregate level data averages","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/center_ipd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","text":"","code":"# load in IPD data(adsl_sat) adsl <- dummize_ipd(adsl_sat, dummize_cols = c(\"SEX\"), dummize_ref_level = c(\"Female\"))  # Reading aggregate data by Excel target_pop <- read.csv(   system.file(\"extdata\", \"aggregate_data_example_1.csv\", package = \"maicplus\", mustWork = TRUE) ) agd <- process_agd(target_pop)  # Alternatively, you can specify aggregate data manually in data frame data(agd) ipd_centered <- center_ipd(ipd = adsl, agd = agd)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/centered_ipd_sat.html","id":null,"dir":"Reference","previous_headings":"","what":"Centered patient data from single arm trial — centered_ipd_sat","title":"Centered patient data from single arm trial — centered_ipd_sat","text":"Centered patient data single arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/centered_ipd_sat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Centered patient data from single arm trial — centered_ipd_sat","text":"","code":"centered_ipd_sat"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/centered_ipd_sat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Centered patient data from single arm trial — centered_ipd_sat","text":"data frame 500 rows 14 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm. AGE Age years baseline. SEX Sex patient recorded character \"Male\"/\"Female\". SMOKE Smoking status baseline integer 1/0. ECOG0 Indicator ECOG score = 0 baseline integer 1/0. N_PR_THER Number prior therapies received integer 1, 2, 3, 4. SEX_MALE Indicator SEX == \"Male\" numeric 1/0. AGE_CENTERED Age years baseline relative average aggregate data agd. AGE_MEDIAN_CENTERED AGE greater/less MEDIAN_AGE agd coded 1/0 centered 0.5. AGE_SQUARED_CENTERED AGE squared centered respect AGE agd. squared age aggregate data derived \\(E(X^2)\\) term variance formula. SEX_MALE_CENTERED SEX_MALE centered proportion male patients agd ECOG0_CENTERED ECOG0 centered proportion ECOG0 agd SMOKE_CENTERED SMOKE centered proportion SMOKE agd N_PR_THER_MEDIAN_CENTERED N_PR_THER centered median agd.","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/centered_ipd_twt.html","id":null,"dir":"Reference","previous_headings":"","what":"Centered patient data from two arm trial — centered_ipd_twt","title":"Centered patient data from two arm trial — centered_ipd_twt","text":"Centered patient data two arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/centered_ipd_twt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Centered patient data from two arm trial — centered_ipd_twt","text":"","code":"centered_ipd_twt"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/centered_ipd_twt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Centered patient data from two arm trial — centered_ipd_twt","text":"data frame 1000 rows 14 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm. AGE Age years baseline. SEX Sex patient recorded character \"Male\"/\"Female\". SMOKE Smoking status baseline integer 1/0. ECOG0 Indicator ECOG score = 0 baseline integer 1/0. N_PR_THER Number prior therapies received integer 1, 2, 3, 4. SEX_MALE Indicator SEX == \"Male\" numeric 1/0. AGE_CENTERED Age years baseline relative average aggregate data agd. AGE_MEDIAN_CENTERED AGE greater/less MEDIAN_AGE agd coded 1/0 centered 0.5. AGE_SQUARED_CENTERED AGE squared centered respect AGE agd. squared age aggregate data derived \\(E(X^2)\\) term variance formula. SEX_MALE_CENTERED SEX_MALE centered proportion male patients agd ECOG0_CENTERED ECOG0 centered proportion ECOG0 agd SMOKE_CENTERED SMOKE centered proportion SMOKE agd N_PR_THER_MEDIAN_CENTERED N_PR_THER centered median agd.","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/check_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Check to see if weights are optimized correctly — check_weights","title":"Check to see if weights are optimized correctly — check_weights","text":"function checks see optimization done properly checking covariate averages adjustment.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/check_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check to see if weights are optimized correctly — check_weights","text":"","code":"check_weights(weighted_data, processed_agd)  # S3 method for maicplus_check_weights print(   x,   mean_digits = 2,   prop_digits = 2,   sd_digits = 3,   digits = getOption(\"digits\"),   ... )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/check_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check to see if weights are optimized correctly — check_weights","text":"weighted_data object returned calculating weights using estimate_weights processed_agd data frame, object returned using process_agd aggregated data following naming convention x object check_weights mean_digits number digits rounding mean columns output prop_digits number digits rounding proportion columns output sd_digits number digits rounding mean columns output digits minimal number significant digits, see print.default. ... arguments print.data.frame","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/check_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check to see if weights are optimized correctly — check_weights","text":"data.frame weighted unweighted covariate averages IPD, average aggregate data, sum inner products covariate \\(x_i\\) weights (\\(exp(x_i\\beta)\\))","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/check_weights.html","id":"methods-by-generic-","dir":"Reference","previous_headings":"","what":"Methods (by generic)","title":"Check to see if weights are optimized correctly — check_weights","text":"print(maicplus_check_weights): Print method check_weights objects","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/check_weights.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check to see if weights are optimized correctly — check_weights","text":"","code":"check_weights(weighted_sat, process_agd(agd)) #>   covariate match_stat internal_trial internal_trial_after_weighted #> 1       AGE       Mean         59.850                         51.00 #> 2       AGE     Median         59.000                         49.00 #> 3       AGE         SD          9.011                          3.25 #> 4  SEX_MALE       Prop          0.380                          0.49 #> 5     ECOG0       Prop          0.410                          0.35 #> 6     SMOKE       Prop          0.320                          0.19 #>   external_trial sum_centered_IPD_with_weights #> 1          51.00                         0.000 #> 2          49.00                         0.000 #> 3           3.25                        -0.004 #> 4           0.49                         0.000 #> 5           0.35                         0.000 #> 6           0.19                         0.000"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/complete_agd.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate pooled arm statistics in Aggregated Data (AgD) based on arm-specific statistics — complete_agd","title":"Calculate pooled arm statistics in Aggregated Data (AgD) based on arm-specific statistics — complete_agd","text":"convenient function pool arm statistics. function called within process_agd ARM equal \"Total\". Note pooled median calculated approximation.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/complete_agd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate pooled arm statistics in Aggregated Data (AgD) based on arm-specific statistics — complete_agd","text":"","code":"complete_agd(use_agd)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/complete_agd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate pooled arm statistics in Aggregated Data (AgD) based on arm-specific statistics — complete_agd","text":"use_agd aggregated data processed within process_agd","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/complete_agd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate pooled arm statistics in Aggregated Data (AgD) based on arm-specific statistics — complete_agd","text":"Complete N, count, mean, sd, median pooled arm","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/dummize_ipd.html","id":null,"dir":"Reference","previous_headings":"","what":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","title":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","text":"convenient function convert categorical variables dummy binary variables. especially useful variable two factors. Note original variable kept variable dummized.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/dummize_ipd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","text":"","code":"dummize_ipd(raw_ipd, dummize_cols, dummize_ref_level)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/dummize_ipd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","text":"raw_ipd ipd data contains variable dummize dummize_cols vector column names binarize dummize_ref_level vector reference level variables binarize","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/dummize_ipd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","text":"ipd dummized columns","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/dummize_ipd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","text":"","code":"data(adsl_twt) dummize_ipd(adsl_twt, dummize_cols = c(\"SEX\"), dummize_ref_level = c(\"Male\")) #>      USUBJID ARM AGE    SEX SMOKE ECOG0 N_PR_THER SEX_MALE SEX_FEMALE #> 1        xx1   A  45   Male     0     0         4        1          0 #> 2        xx2   A  71   Male     0     0         3        1          0 #> 3        xx3   A  58   Male     1     1         2        1          0 #> 4        xx4   A  48 Female     0     1         4        0          1 #> 5        xx5   A  69   Male     0     1         4        1          0 #> 6        xx6   A  48 Female     0     1         4        0          1 #> 7        xx7   A  47   Male     1     0         3        1          0 #> 8        xx8   A  61   Male     1     0         1        1          0 #> 9        xx9   A  54 Female     1     1         1        0          1 #> 10      xx10   A  56 Female     1     0         3        0          1 #> 11      xx11   A  63 Female     0     0         4        0          1 #> 12      xx12   A  50 Female     0     0         1        0          1 #> 13      xx13   A  57   Male     0     1         3        1          0 #> 14      xx14   A  62 Female     1     1         1        0          1 #> 15      xx15   A  57 Female     0     1         3        0          1 #> 16      xx16   A  66   Male     0     0         2        1          0 #> 17      xx17   A  75   Male     1     1         3        1          0 #> 18      xx18   A  47 Female     0     0         4        0          1 #> 19      xx19   A  57   Male     0     0         3        1          0 #> 20      xx20   A  54   Male     0     0         3        1          0 #> 21      xx21   A  55   Male     1     0         3        1          0 #> 22      xx22   A  64   Male     0     1         3        1          0 #> 23      xx23   A  53 Female     1     0         3        0          1 #> 24      xx24   A  58   Male     1     1         2        1          0 #> 25      xx25   A  47   Male     0     0         1        1          0 #> 26      xx26   A  60 Female     1     0         1        0          1 #> 27      xx27   A  49 Female     0     1         3        0          1 #> 28      xx28   A  55 Female     0     0         1        0          1 #> 29      xx29   A  66 Female     0     1         2        0          1 #> 30      xx30   A  58   Male     0     1         4        1          0 #> 31      xx31   A  49   Male     0     1         4        1          0 #> 32      xx32   A  61   Male     0     0         4        1          0 #> 33      xx33   A  66   Male     1     0         3        1          0 #> 34      xx34   A  45   Male     0     0         1        1          0 #> 35      xx35   A  59 Female     1     1         2        0          1 #> 36      xx36   A  74 Female     1     0         4        0          1 #> 37      xx37   A  73 Female     0     0         3        0          1 #> 38      xx38   A  74   Male     0     1         4        1          0 #> 39      xx39   A  54   Male     0     0         1        1          0 #> 40      xx40   A  58 Female     1     1         1        0          1 #> 41      xx41   A  61 Female     0     1         3        0          1 #> 42      xx42   A  47 Female     1     1         2        0          1 #> 43      xx43   A  73 Female     1     1         2        0          1 #> 44      xx44   A  68   Male     0     0         1        1          0 #> 45      xx45   A  49 Female     0     0         3        0          1 #> 46      xx46   A  71 Female     0     0         2        0          1 #> 47      xx47   A  70   Male     0     1         4        1          0 #> 48      xx48   A  62 Female     1     0         1        0          1 #> 49      xx49   A  49   Male     0     0         1        1          0 #> 50      xx50   A  74 Female     0     0         1        0          1 #> 51      xx51   A  46 Female     0     1         3        0          1 #> 52      xx52   A  68 Female     1     0         3        0          1 #> 53      xx53   A  46   Male     1     0         2        1          0 #> 54      xx54   A  75 Female     1     1         3        0          1 #> 55      xx55   A  47 Female     0     0         3        0          1 #> 56      xx56   A  56   Male     0     1         3        1          0 #> 57      xx57   A  72 Female     0     0         3        0          1 #> 58      xx58   A  57   Male     1     1         4        1          0 #> 59      xx59   A  46   Male     0     0         1        1          0 #> 60      xx60   A  56 Female     1     1         1        0          1 #> 61      xx61   A  73   Male     0     1         2        1          0 #> 62      xx62   A  60 Female     1     1         3        0          1 #> 63      xx63   A  75   Male     0     0         2        1          0 #> 64      xx64   A  69 Female     1     1         2        0          1 #> 65      xx65   A  47 Female     0     1         1        0          1 #> 66      xx66   A  74   Male     0     0         4        1          0 #> 67      xx67   A  71 Female     0     1         1        0          1 #> 68      xx68   A  49 Female     1     1         1        0          1 #> 69      xx69   A  68   Male     0     0         3        1          0 #> 70      xx70   A  49   Male     0     1         1        1          0 #> 71      xx71   A  70   Male     0     1         1        1          0 #> 72      xx72   A  45 Female     0     0         2        0          1 #> 73      xx73   A  47 Female     0     1         3        0          1 #> 74      xx74   A  58   Male     0     1         3        1          0 #> 75      xx75   A  49 Female     0     1         4        0          1 #> 76      xx76   A  68 Female     0     0         1        0          1 #> 77      xx77   A  60   Male     0     0         4        1          0 #> 78      xx78   A  45 Female     1     0         1        0          1 #> 79      xx79   A  57 Female     0     0         1        0          1 #> 80      xx80   A  50 Female     0     1         1        0          1 #> 81      xx81   A  63   Male     0     1         3        1          0 #> 82      xx82   A  47 Female     0     0         2        0          1 #> 83      xx83   A  68 Female     0     1         4        0          1 #> 84      xx84   A  51   Male     0     0         4        1          0 #> 85      xx85   A  60   Male     0     0         1        1          0 #> 86      xx86   A  52 Female     1     0         4        0          1 #> 87      xx87   A  69   Male     1     1         1        1          0 #> 88      xx88   A  70 Female     0     1         4        0          1 #> 89      xx89   A  72   Male     0     0         2        1          0 #> 90      xx90   A  46 Female     0     1         1        0          1 #> 91      xx91   A  51   Male     1     0         4        1          0 #> 92      xx92   A  69 Female     0     1         1        0          1 #> 93      xx93   A  66   Male     0     0         1        1          0 #> 94      xx94   A  73   Male     0     1         1        1          0 #> 95      xx95   A  73 Female     1     0         3        0          1 #> 96      xx96   A  62 Female     0     1         2        0          1 #> 97      xx97   A  55 Female     0     0         4        0          1 #> 98      xx98   A  67   Male     1     0         3        1          0 #> 99      xx99   A  54 Female     1     0         3        0          1 #> 100    xx100   A  52 Female     1     1         4        0          1 #> 101    xx101   A  57   Male     0     0         2        1          0 #> 102    xx102   A  57 Female     1     1         3        0          1 #> 103    xx103   A  57   Male     0     0         3        1          0 #> 104    xx104   A  67 Female     1     1         2        0          1 #> 105    xx105   A  67 Female     1     1         2        0          1 #> 106    xx106   A  74 Female     1     1         2        0          1 #> 107    xx107   A  72 Female     1     0         2        0          1 #> 108    xx108   A  73 Female     0     0         3        0          1 #> 109    xx109   A  57 Female     0     0         4        0          1 #> 110    xx110   A  69 Female     1     0         1        0          1 #> 111    xx111   A  55   Male     0     0         1        1          0 #> 112    xx112   A  74 Female     0     0         4        0          1 #> 113    xx113   A  68 Female     0     0         4        0          1 #> 114    xx114   A  53   Male     0     0         2        1          0 #> 115    xx115   A  69   Male     0     0         2        1          0 #> 116    xx116   A  68   Male     0     1         2        1          0 #> 117    xx117   A  58   Male     0     0         1        1          0 #> 118    xx118   A  64 Female     0     0         3        0          1 #> 119    xx119   A  71   Male     0     0         1        1          0 #> 120    xx120   A  69 Female     0     1         2        0          1 #> 121    xx121   A  64 Female     1     0         4        0          1 #> 122    xx122   A  64   Male     1     0         1        1          0 #> 123    xx123   A  55   Male     0     1         3        1          0 #> 124    xx124   A  74   Male     0     0         3        1          0 #> 125    xx125   A  50   Male     0     1         3        1          0 #> 126    xx126   A  68   Male     0     1         1        1          0 #> 127    xx127   A  60   Male     0     0         2        1          0 #> 128    xx128   A  59 Female     0     0         3        0          1 #> 129    xx129   A  71 Female     1     0         3        0          1 #> 130    xx130   A  69   Male     0     1         4        1          0 #> 131    xx131   A  56 Female     0     1         1        0          1 #> 132    xx132   A  51   Male     0     1         3        1          0 #> 133    xx133   A  65   Male     0     1         2        1          0 #> 134    xx134   A  45   Male     1     1         3        1          0 #> 135    xx135   A  49 Female     0     0         2        0          1 #> 136    xx136   A  74 Female     1     0         4        0          1 #> 137    xx137   A  71 Female     1     1         3        0          1 #> 138    xx138   A  65   Male     0     0         1        1          0 #> 139    xx139   A  67 Female     0     0         2        0          1 #> 140    xx140   A  48 Female     0     0         3        0          1 #> 141    xx141   A  70 Female     0     1         4        0          1 #> 142    xx142   A  72 Female     0     0         1        0          1 #> 143    xx143   A  53   Male     1     1         4        1          0 #> 144    xx144   A  68 Female     1     0         3        0          1 #> 145    xx145   A  65   Male     0     1         1        1          0 #> 146    xx146   A  70 Female     1     1         4        0          1 #> 147    xx147   A  58   Male     0     0         2        1          0 #> 148    xx148   A  68 Female     0     1         4        0          1 #> 149    xx149   A  54 Female     0     0         4        0          1 #> 150    xx150   A  75 Female     1     1         4        0          1 #> 151    xx151   A  49 Female     1     1         3        0          1 #> 152    xx152   A  60   Male     0     0         4        1          0 #> 153    xx153   A  45 Female     1     0         4        0          1 #> 154    xx154   A  73 Female     1     0         3        0          1 #> 155    xx155   A  71 Female     0     1         3        0          1 #> 156    xx156   A  73 Female     0     0         3        0          1 #> 157    xx157   A  58 Female     0     0         4        0          1 #> 158    xx158   A  59 Female     0     1         2        0          1 #> 159    xx159   A  75 Female     0     1         2        0          1 #> 160    xx160   A  65   Male     0     0         2        1          0 #> 161    xx161   A  48   Male     1     0         2        1          0 #> 162    xx162   A  63 Female     0     1         2        0          1 #> 163    xx163   A  74 Female     0     0         3        0          1 #> 164    xx164   A  64 Female     0     0         3        0          1 #> 165    xx165   A  49 Female     0     0         1        0          1 #> 166    xx166   A  56 Female     0     1         4        0          1 #> 167    xx167   A  56 Female     0     1         2        0          1 #> 168    xx168   A  48 Female     0     0         1        0          1 #> 169    xx169   A  65   Male     0     1         3        1          0 #> 170    xx170   A  53 Female     0     0         3        0          1 #> 171    xx171   A  72   Male     0     0         2        1          0 #> 172    xx172   A  75 Female     1     0         4        0          1 #> 173    xx173   A  70 Female     0     1         2        0          1 #> 174    xx174   A  53 Female     1     0         2        0          1 #> 175    xx175   A  45 Female     1     1         3        0          1 #> 176    xx176   A  53 Female     1     1         3        0          1 #> 177    xx177   A  52 Female     0     1         2        0          1 #> 178    xx178   A  61 Female     1     0         4        0          1 #> 179    xx179   A  70   Male     0     0         3        1          0 #> 180    xx180   A  58 Female     0     0         3        0          1 #> 181    xx181   A  54   Male     0     0         1        1          0 #> 182    xx182   A  53   Male     0     0         2        1          0 #> 183    xx183   A  74   Male     0     0         2        1          0 #> 184    xx184   A  64   Male     0     1         3        1          0 #> 185    xx185   A  52   Male     0     0         1        1          0 #> 186    xx186   A  73 Female     0     0         3        0          1 #> 187    xx187   A  55 Female     1     0         4        0          1 #> 188    xx188   A  71 Female     0     0         3        0          1 #> 189    xx189   A  57 Female     1     0         3        0          1 #> 190    xx190   A  49 Female     1     0         2        0          1 #> 191    xx191   A  69   Male     0     0         4        1          0 #> 192    xx192   A  74 Female     1     0         2        0          1 #> 193    xx193   A  59 Female     0     0         3        0          1 #> 194    xx194   A  53   Male     0     0         4        1          0 #> 195    xx195   A  52 Female     0     1         3        0          1 #> 196    xx196   A  47 Female     1     1         1        0          1 #> 197    xx197   A  61 Female     0     0         4        0          1 #> 198    xx198   A  51 Female     0     0         2        0          1 #> 199    xx199   A  62 Female     1     0         1        0          1 #> 200    xx200   A  59 Female     1     0         2        0          1 #> 201    xx201   A  58   Male     0     0         2        1          0 #> 202    xx202   A  61 Female     0     1         4        0          1 #> 203    xx203   A  45 Female     0     0         1        0          1 #> 204    xx204   A  59   Male     1     1         2        1          0 #> 205    xx205   A  58 Female     1     0         2        0          1 #> 206    xx206   A  67   Male     0     0         1        1          0 #> 207    xx207   A  51 Female     1     0         2        0          1 #> 208    xx208   A  68   Male     1     1         3        1          0 #> 209    xx209   A  53 Female     1     0         2        0          1 #> 210    xx210   A  64   Male     1     0         3        1          0 #> 211    xx211   A  61   Male     0     1         1        1          0 #> 212    xx212   A  52   Male     1     0         2        1          0 #> 213    xx213   A  57 Female     0     1         4        0          1 #> 214    xx214   A  57 Female     1     0         2        0          1 #> 215    xx215   A  49 Female     0     0         4        0          1 #> 216    xx216   A  51   Male     1     1         3        1          0 #> 217    xx217   A  60 Female     0     0         2        0          1 #> 218    xx218   A  60 Female     1     0         1        0          1 #> 219    xx219   A  71 Female     0     0         3        0          1 #> 220    xx220   A  54 Female     0     0         1        0          1 #> 221    xx221   A  51   Male     1     0         1        1          0 #> 222    xx222   A  71   Male     0     1         3        1          0 #> 223    xx223   A  47   Male     0     0         2        1          0 #> 224    xx224   A  65   Male     0     0         1        1          0 #> 225    xx225   A  53 Female     0     0         1        0          1 #> 226    xx226   A  56 Female     0     0         1        0          1 #> 227    xx227   A  51   Male     1     0         4        1          0 #> 228    xx228   A  68 Female     0     0         2        0          1 #> 229    xx229   A  75 Female     0     1         1        0          1 #> 230    xx230   A  49 Female     0     0         2        0          1 #> 231    xx231   A  74   Male     1     0         1        1          0 #> 232    xx232   A  66 Female     0     0         1        0          1 #> 233    xx233   A  74 Female     0     0         1        0          1 #> 234    xx234   A  62 Female     0     1         2        0          1 #> 235    xx235   A  53 Female     0     0         1        0          1 #> 236    xx236   A  62 Female     0     1         1        0          1 #> 237    xx237   A  70 Female     1     0         3        0          1 #> 238    xx238   A  60 Female     1     0         2        0          1 #> 239    xx239   A  72   Male     0     0         1        1          0 #> 240    xx240   A  74 Female     1     0         3        0          1 #> 241    xx241   A  47 Female     1     0         3        0          1 #> 242    xx242   A  54 Female     0     1         3        0          1 #> 243    xx243   A  65 Female     0     1         2        0          1 #> 244    xx244   A  74   Male     0     1         1        1          0 #> 245    xx245   A  61   Male     0     1         2        1          0 #> 246    xx246   A  54 Female     1     0         3        0          1 #> 247    xx247   A  65   Male     0     0         3        1          0 #> 248    xx248   A  72 Female     1     0         3        0          1 #> 249    xx249   A  71 Female     1     1         2        0          1 #> 250    xx250   A  65   Male     0     0         3        1          0 #> 251    xx251   A  58   Male     0     1         1        1          0 #> 252    xx252   A  46   Male     0     0         1        1          0 #> 253    xx253   A  53 Female     1     0         4        0          1 #> 254    xx254   A  71 Female     1     0         4        0          1 #> 255    xx255   A  47   Male     0     1         3        1          0 #> 256    xx256   A  45   Male     0     0         1        1          0 #> 257    xx257   A  50   Male     0     0         3        1          0 #> 258    xx258   A  67 Female     1     0         3        0          1 #> 259    xx259   A  72   Male     1     0         2        1          0 #> 260    xx260   A  45 Female     0     1         2        0          1 #> 261    xx261   A  75 Female     1     0         1        0          1 #> 262    xx262   A  65   Male     0     0         3        1          0 #> 263    xx263   A  60 Female     0     1         3        0          1 #> 264    xx264   A  75 Female     0     1         4        0          1 #> 265    xx265   A  60 Female     1     0         4        0          1 #> 266    xx266   A  49 Female     1     0         2        0          1 #> 267    xx267   A  58 Female     0     1         1        0          1 #> 268    xx268   A  57   Male     1     1         3        1          0 #> 269    xx269   A  69   Male     1     0         2        1          0 #> 270    xx270   A  51   Male     0     1         1        1          0 #> 271    xx271   A  54 Female     1     0         4        0          1 #> 272    xx272   A  55   Male     0     1         3        1          0 #> 273    xx273   A  49 Female     0     0         4        0          1 #> 274    xx274   A  74 Female     1     0         1        0          1 #> 275    xx275   A  55   Male     1     0         1        1          0 #> 276    xx276   A  52 Female     1     0         1        0          1 #> 277    xx277   A  65   Male     0     0         2        1          0 #> 278    xx278   A  70 Female     1     0         1        0          1 #> 279    xx279   A  66 Female     1     1         2        0          1 #> 280    xx280   A  63 Female     0     1         4        0          1 #> 281    xx281   A  61 Female     0     1         3        0          1 #> 282    xx282   A  65   Male     0     1         2        1          0 #> 283    xx283   A  73   Male     0     0         2        1          0 #> 284    xx284   A  55 Female     1     1         4        0          1 #> 285    xx285   A  56 Female     1     1         4        0          1 #> 286    xx286   A  68 Female     0     1         1        0          1 #> 287    xx287   A  74 Female     1     0         4        0          1 #> 288    xx288   A  67 Female     0     0         2        0          1 #> 289    xx289   A  66   Male     0     1         3        1          0 #> 290    xx290   A  48 Female     0     0         3        0          1 #> 291    xx291   A  49 Female     1     1         3        0          1 #> 292    xx292   A  60 Female     1     1         1        0          1 #> 293    xx293   A  69 Female     0     1         4        0          1 #> 294    xx294   A  58 Female     0     1         3        0          1 #> 295    xx295   A  45 Female     1     1         4        0          1 #> 296    xx296   A  49 Female     0     1         1        0          1 #> 297    xx297   A  67 Female     1     0         4        0          1 #> 298    xx298   A  63   Male     0     0         4        1          0 #> 299    xx299   A  50 Female     0     0         2        0          1 #> 300    xx300   A  68 Female     0     1         3        0          1 #> 301    xx301   A  53   Male     0     1         2        1          0 #> 302    xx302   A  63   Male     0     1         2        1          0 #> 303    xx303   A  58   Male     0     0         4        1          0 #> 304    xx304   A  70 Female     0     1         4        0          1 #> 305    xx305   A  56 Female     0     0         1        0          1 #> 306    xx306   A  56   Male     0     1         3        1          0 #> 307    xx307   A  61 Female     0     0         3        0          1 #> 308    xx308   A  72   Male     0     0         2        1          0 #> 309    xx309   A  51   Male     0     1         1        1          0 #> 310    xx310   A  72   Male     0     0         4        1          0 #> 311    xx311   A  64 Female     0     0         3        0          1 #> 312    xx312   A  59   Male     0     0         2        1          0 #> 313    xx313   A  75 Female     0     0         3        0          1 #> 314    xx314   A  75 Female     0     0         1        0          1 #> 315    xx315   A  74   Male     0     0         2        1          0 #> 316    xx316   A  54   Male     0     1         4        1          0 #> 317    xx317   A  55 Female     0     0         3        0          1 #> 318    xx318   A  52 Female     1     0         1        0          1 #> 319    xx319   A  46 Female     0     1         1        0          1 #> 320    xx320   A  53   Male     0     0         1        1          0 #> 321    xx321   A  54 Female     0     1         1        0          1 #> 322    xx322   A  62 Female     0     0         2        0          1 #> 323    xx323   A  54   Male     1     0         4        1          0 #> 324    xx324   A  56 Female     0     0         4        0          1 #> 325    xx325   A  48 Female     0     0         1        0          1 #> 326    xx326   A  52 Female     0     1         1        0          1 #> 327    xx327   A  55 Female     0     1         3        0          1 #> 328    xx328   A  69 Female     0     0         2        0          1 #> 329    xx329   A  48 Female     0     0         1        0          1 #> 330    xx330   A  48 Female     1     1         3        0          1 #> 331    xx331   A  60   Male     0     0         3        1          0 #> 332    xx332   A  74 Female     0     1         2        0          1 #> 333    xx333   A  45 Female     0     0         1        0          1 #> 334    xx334   A  64   Male     1     0         1        1          0 #> 335    xx335   A  75 Female     1     1         3        0          1 #> 336    xx336   A  62 Female     0     0         3        0          1 #> 337    xx337   A  71   Male     0     0         4        1          0 #> 338    xx338   A  48 Female     1     1         3        0          1 #> 339    xx339   A  53 Female     0     0         2        0          1 #> 340    xx340   A  62   Male     0     0         3        1          0 #> 341    xx341   A  69 Female     0     0         2        0          1 #> 342    xx342   A  72 Female     0     0         1        0          1 #> 343    xx343   A  61 Female     0     0         2        0          1 #> 344    xx344   A  47   Male     0     0         2        1          0 #> 345    xx345   A  58   Male     0     1         1        1          0 #> 346    xx346   A  52 Female     0     0         4        0          1 #> 347    xx347   A  49 Female     0     0         4        0          1 #> 348    xx348   A  51 Female     0     0         1        0          1 #> 349    xx349   A  51 Female     1     1         2        0          1 #> 350    xx350   A  72   Male     0     0         3        1          0 #> 351    xx351   A  68   Male     0     0         3        1          0 #> 352    xx352   A  49 Female     0     0         4        0          1 #> 353    xx353   A  45 Female     0     0         3        0          1 #> 354    xx354   A  49 Female     0     0         3        0          1 #> 355    xx355   A  65   Male     0     1         2        1          0 #> 356    xx356   A  56   Male     0     0         2        1          0 #> 357    xx357   A  45 Female     1     1         1        0          1 #> 358    xx358   A  57   Male     1     0         2        1          0 #> 359    xx359   A  53   Male     1     1         2        1          0 #> 360    xx360   A  65 Female     0     0         2        0          1 #> 361    xx361   A  57   Male     0     1         4        1          0 #> 362    xx362   A  55 Female     0     1         4        0          1 #> 363    xx363   A  57   Male     0     1         2        1          0 #> 364    xx364   A  46 Female     0     1         4        0          1 #> 365    xx365   A  69 Female     0     1         1        0          1 #> 366    xx366   A  67 Female     0     1         3        0          1 #> 367    xx367   A  55   Male     0     0         1        1          0 #> 368    xx368   A  53 Female     0     1         3        0          1 #> 369    xx369   A  46 Female     0     0         3        0          1 #> 370    xx370   A  71   Male     0     0         4        1          0 #> 371    xx371   A  68   Male     0     1         1        1          0 #> 372    xx372   A  49 Female     0     1         3        0          1 #> 373    xx373   A  51 Female     0     0         3        0          1 #> 374    xx374   A  65 Female     1     1         3        0          1 #> 375    xx375   A  55 Female     0     0         4        0          1 #> 376    xx376   A  53   Male     0     0         4        1          0 #> 377    xx377   A  65 Female     0     1         4        0          1 #> 378    xx378   A  72 Female     1     1         4        0          1 #> 379    xx379   A  61   Male     0     1         1        1          0 #> 380    xx380   A  73 Female     0     0         1        0          1 #> 381    xx381   A  62 Female     1     1         2        0          1 #> 382    xx382   A  46   Male     0     0         2        1          0 #> 383    xx383   A  51   Male     0     1         4        1          0 #> 384    xx384   A  60   Male     1     1         4        1          0 #> 385    xx385   A  56 Female     0     1         3        0          1 #> 386    xx386   A  69 Female     0     1         1        0          1 #> 387    xx387   A  58 Female     1     1         2        0          1 #> 388    xx388   A  58 Female     1     1         3        0          1 #> 389    xx389   A  53 Female     0     1         2        0          1 #> 390    xx390   A  47 Female     0     0         2        0          1 #> 391    xx391   A  59   Male     0     1         2        1          0 #> 392    xx392   A  47 Female     0     0         4        0          1 #> 393    xx393   A  60 Female     1     0         4        0          1 #> 394    xx394   A  73 Female     0     0         4        0          1 #> 395    xx395   A  60   Male     0     0         1        1          0 #> 396    xx396   A  75   Male     0     0         4        1          0 #> 397    xx397   A  65 Female     0     0         3        0          1 #> 398    xx398   A  68   Male     0     0         1        1          0 #> 399    xx399   A  55 Female     0     0         4        0          1 #> 400    xx400   A  46 Female     1     0         4        0          1 #> 401    xx401   A  45 Female     0     1         1        0          1 #> 402    xx402   A  70   Male     1     1         3        1          0 #> 403    xx403   A  56 Female     0     1         4        0          1 #> 404    xx404   A  62 Female     0     0         3        0          1 #> 405    xx405   A  49   Male     0     0         4        1          0 #> 406    xx406   A  52 Female     0     0         4        0          1 #> 407    xx407   A  67 Female     0     1         4        0          1 #> 408    xx408   A  50 Female     1     1         1        0          1 #> 409    xx409   A  68 Female     1     1         2        0          1 #> 410    xx410   A  54 Female     0     0         4        0          1 #> 411    xx411   A  65   Male     0     0         4        1          0 #> 412    xx412   A  55 Female     0     1         2        0          1 #> 413    xx413   A  53 Female     1     1         4        0          1 #> 414    xx414   A  71 Female     0     0         2        0          1 #> 415    xx415   A  48   Male     0     0         1        1          0 #> 416    xx416   A  54 Female     0     1         3        0          1 #> 417    xx417   A  75   Male     1     0         3        1          0 #> 418    xx418   A  53 Female     0     0         2        0          1 #> 419    xx419   A  50 Female     1     0         4        0          1 #> 420    xx420   A  64 Female     1     0         1        0          1 #> 421    xx421   A  65 Female     0     0         2        0          1 #> 422    xx422   A  65 Female     0     1         4        0          1 #> 423    xx423   A  60 Female     1     0         2        0          1 #> 424    xx424   A  70 Female     1     0         3        0          1 #> 425    xx425   A  51 Female     0     0         2        0          1 #> 426    xx426   A  45 Female     0     1         1        0          1 #> 427    xx427   A  75 Female     1     0         2        0          1 #> 428    xx428   A  52 Female     1     0         1        0          1 #> 429    xx429   A  70   Male     0     0         4        1          0 #> 430    xx430   A  69 Female     1     1         3        0          1 #> 431    xx431   A  64 Female     0     0         2        0          1 #> 432    xx432   A  68 Female     1     0         1        0          1 #> 433    xx433   A  51   Male     1     0         1        1          0 #> 434    xx434   A  59 Female     0     1         2        0          1 #> 435    xx435   A  57 Female     0     0         1        0          1 #> 436    xx436   A  47   Male     0     0         2        1          0 #> 437    xx437   A  65   Male     0     1         1        1          0 #> 438    xx438   A  65   Male     0     1         1        1          0 #> 439    xx439   A  65   Male     0     0         2        1          0 #> 440    xx440   A  46   Male     0     0         2        1          0 #> 441    xx441   A  64 Female     0     0         3        0          1 #> 442    xx442   A  57 Female     0     1         4        0          1 #> 443    xx443   A  67 Female     0     1         3        0          1 #> 444    xx444   A  61 Female     1     0         3        0          1 #> 445    xx445   A  56   Male     0     0         4        1          0 #> 446    xx446   A  52   Male     0     0         3        1          0 #> 447    xx447   A  74 Female     1     1         3        0          1 #> 448    xx448   A  75   Male     0     1         3        1          0 #> 449    xx449   A  58   Male     0     1         3        1          0 #> 450    xx450   A  57 Female     0     1         4        0          1 #> 451    xx451   A  55 Female     0     0         1        0          1 #> 452    xx452   A  53 Female     1     0         1        0          1 #> 453    xx453   A  75   Male     1     0         4        1          0 #> 454    xx454   A  65 Female     0     1         3        0          1 #> 455    xx455   A  65 Female     0     0         4        0          1 #> 456    xx456   A  58   Male     0     0         4        1          0 #> 457    xx457   A  71 Female     0     0         3        0          1 #> 458    xx458   A  71   Male     0     0         4        1          0 #> 459    xx459   A  59   Male     1     0         4        1          0 #> 460    xx460   A  46   Male     1     0         4        1          0 #> 461    xx461   A  51 Female     0     0         4        0          1 #> 462    xx462   A  56 Female     0     1         4        0          1 #> 463    xx463   A  66 Female     0     0         1        0          1 #> 464    xx464   A  59 Female     1     0         2        0          1 #> 465    xx465   A  48 Female     1     0         2        0          1 #> 466    xx466   A  68 Female     0     0         4        0          1 #> 467    xx467   A  57 Female     0     0         4        0          1 #> 468    xx468   A  63   Male     0     0         4        1          0 #> 469    xx469   A  62   Male     0     1         3        1          0 #> 470    xx470   A  70 Female     0     0         3        0          1 #> 471    xx471   A  55 Female     0     1         2        0          1 #> 472    xx472   A  56   Male     1     0         3        1          0 #> 473    xx473   A  51   Male     1     0         1        1          0 #> 474    xx474   A  51 Female     0     0         4        0          1 #> 475    xx475   A  46 Female     1     1         3        0          1 #> 476    xx476   A  52   Male     0     0         4        1          0 #> 477    xx477   A  71   Male     0     1         4        1          0 #> 478    xx478   A  54   Male     1     0         3        1          0 #> 479    xx479   A  55   Male     0     1         2        1          0 #> 480    xx480   A  46 Female     0     0         3        0          1 #> 481    xx481   A  70 Female     0     0         1        0          1 #> 482    xx482   A  68 Female     0     1         3        0          1 #> 483    xx483   A  50   Male     0     1         1        1          0 #> 484    xx484   A  45 Female     0     0         2        0          1 #> 485    xx485   A  68   Male     0     0         1        1          0 #> 486    xx486   A  56   Male     0     1         2        1          0 #> 487    xx487   A  59   Male     0     1         3        1          0 #> 488    xx488   A  51   Male     1     1         1        1          0 #> 489    xx489   A  61 Female     1     0         3        0          1 #> 490    xx490   A  60 Female     0     1         3        0          1 #> 491    xx491   A  68 Female     1     0         2        0          1 #> 492    xx492   A  67   Male     1     1         4        1          0 #> 493    xx493   A  45   Male     1     1         2        1          0 #> 494    xx494   A  71 Female     0     1         2        0          1 #> 495    xx495   A  55   Male     0     1         3        1          0 #> 496    xx496   A  72 Female     0     0         4        0          1 #> 497    xx497   A  48 Female     1     0         3        0          1 #> 498    xx498   A  68 Female     1     0         1        0          1 #> 499    xx499   A  45 Female     0     0         1        0          1 #> 500    xx500   A  58 Female     1     1         3        0          1 #> 1721   yyxx1   C  75 Female     1     0         4        0          1 #> 3651  yyxx10   C  69 Female     0     1         1        0          1 #> 4881 yyxx100   C  51   Male     1     1         1        1          0 #> 2281 yyxx101   C  68 Female     0     0         2        0          1 #> 4231 yyxx102   C  60 Female     1     0         2        0          1 #> 1981 yyxx103   C  51 Female     0     0         2        0          1 #> 2401 yyxx104   C  74 Female     1     0         3        0          1 #> 910  yyxx105   C  54 Female     1     1         1        0          1 #> 4410 yyxx106   C  68   Male     0     0         1        1          0 #> 2221 yyxx107   C  71   Male     0     1         3        1          0 #> 741  yyxx108   C  58   Male     0     1         3        1          0 #> 710  yyxx109   C  47   Male     1     0         3        1          0 #> 1310  yyxx11   C  57   Male     0     1         3        1          0 #> 3571 yyxx110   C  45 Female     1     1         1        0          1 #> 2551 yyxx111   C  47   Male     0     1         3        1          0 #> 3831 yyxx112   C  51   Male     0     1         4        1          0 #> 1121 yyxx113   C  74 Female     0     0         4        0          1 #> 4841 yyxx114   C  45 Female     0     0         2        0          1 #> 731  yyxx115   C  47 Female     0     1         3        0          1 #> 4771 yyxx116   C  71   Male     0     1         4        1          0 #> 4371 yyxx117   C  65   Male     0     1         1        1          0 #> 971  yyxx118   C  55 Female     0     0         4        0          1 #> 1451 yyxx119   C  65   Male     0     1         1        1          0 #> 2701  yyxx12   C  51   Male     0     1         1        1          0 #> 3471 yyxx120   C  49 Female     0     0         4        0          1 #> 3781 yyxx121   C  72 Female     1     1         4        0          1 #> 1010 yyxx122   C 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1 #> 4710 yyxx135   C  70   Male     0     1         4        1          0 #> 4281 yyxx136   C  52 Female     1     0         1        0          1 #> 1761 yyxx137   C  53 Female     1     1         3        0          1 #> 4791 yyxx138   C  55   Male     0     1         2        1          0 #> 2251 yyxx139   C  53 Female     0     0         1        0          1 #> 3341  yyxx14   C  64   Male     1     0         1        1          0 #> 1031 yyxx140   C  57   Male     0     0         3        1          0 #> 4001 yyxx141   C  46 Female     1     0         4        0          1 #> 4381 yyxx142   C  65   Male     0     1         1        1          0 #> 2831 yyxx143   C  73   Male     0     0         2        1          0 #> 2271 yyxx144   C  51   Male     1     0         4        1          0 #> 4021 yyxx145   C  70   Male     1     1         3        1          0 #> 2941 yyxx146   C  58 Female     0     1         3        0          1 #> 1021 yyxx147   C  57 Female     1     1        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Male     1     0         4        1          0 #> 2671 yyxx160   C  58 Female     0     1         1        0          1 #> 2011 yyxx161   C  58   Male     0     0         2        1          0 #> 1011 yyxx162   C  57   Male     0     0         2        1          0 #> 1851 yyxx163   C  52   Male     0     0         1        1          0 #> 1481 yyxx164   C  68 Female     0     1         4        0          1 #> 3981 yyxx165   C  68   Male     0     0         1        1          0 #> 4521 yyxx166   C  53 Female     1     0         1        0          1 #> 1711 yyxx167   C  72   Male     0     0         2        1          0 #> 3741 yyxx168   C  65 Female     1     1         3        0          1 #> 4671 yyxx169   C  57 Female     0     0         4        0          1 #> 3001  yyxx17   C  68 Female     0     1         3        0          1 #> 3581 yyxx170   C  57   Male     1     0         2        1          0 #> 2611 yyxx171   C  75 Female     1     0         1        0          1 #> 2110 yyxx172   C  55   Male     1     0         3        1          0 #> 1071 yyxx173   C  72 Female     1     0         2        0          1 #> 3331 yyxx174   C  45 Female     0     0         1        0          1 #> 2121 yyxx175   C  52   Male     1     0         2        1          0 #> 2361 yyxx176   C  62 Female     0     1         1        0          1 #> 4511 yyxx177   C  55 Female     0     0         1        0          1 #> 2371 yyxx178   C  70 Female     1     0         3        0          1 #> 2381 yyxx179   C  60 Female     1     0         2        0          1 #> 4591  yyxx18   C  59   Male     1     0         4        1          0 #> 3791 yyxx180   C  61   Male     0     1         1        1          0 #> 2851 yyxx181   C  56 Female     1     1         4        0          1 #> 4941 yyxx182   C  71 Female     0     1         2        0          1 #> 4071 yyxx183   C  67 Female     0     1         4        0          1 #> 3291 yyxx184   C  48 Female     0     0         1        0          1 #> 1631 yyxx185   C  74 Female     0     0         3        0          1 #> 711  yyxx186   C  70   Male     0     1         1        1          0 #> 1301 yyxx187   C  69   Male     0     1         4        1          0 #> 4100 yyxx188   C  48 Female     0     1         4        0          1 #> 841  yyxx189   C  51   Male     0     0         4        1          0 #> 2321  yyxx19   C  66 Female     0     0         1        0          1 #> 1081 yyxx190   C  73 Female     0     0         3        0          1 #> 4041 yyxx191   C  62 Female     0     0         3        0          1 #> 4661 yyxx192   C  68 Female     0     0         4        0          1 #> 631  yyxx193   C  75   Male     0     0         2        1          0 #> 1241 yyxx194   C  74   Male     0     0         3        1          0 #> 3171 yyxx195   C  55 Female     0     0         3        0          1 #> 3441 yyxx196   C  47   Male     0     0         2        1          0 #> 2091 yyxx197   C  53 Female    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yyxx209   C  66   Male     0     0         1        1          0 #> 3111  yyxx21   C  64 Female     0     0         3        0          1 #> 4351 yyxx210   C  57 Female     0     0         1        0          1 #> 811  yyxx211   C  63   Male     0     1         3        1          0 #> 511  yyxx212   C  46 Female     0     1         3        0          1 #> 2510 yyxx213   C  47   Male     0     0         1        1          0 #> 3911 yyxx214   C  59   Male     0     1         2        1          0 #> 4641 yyxx215   C  59 Female     1     0         2        0          1 #> 3410 yyxx216   C  45   Male     0     0         1        1          0 #> 551  yyxx217   C  47 Female     0     0         3        0          1 #> 2911 yyxx218   C  49 Female     1     1         3        0          1 #> 2581 yyxx219   C  67 Female     1     0         3        0          1 #> 4011  yyxx22   C  45 Female     0     1         1        0          1 #> 2511 yyxx220   C  58   Male     0     1         1        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C  54   Male     1     0         3        1          0 #> 1661 yyxx247   C  56 Female     0     1         4        0          1 #> 4751 yyxx248   C  46 Female     1     1         3        0          1 #> 1601 yyxx249   C  65   Male     0     0         2        1          0 #> 1891  yyxx25   C  57 Female     1     0         3        0          1 #> 3961 yyxx250   C  75   Male     0     0         4        1          0 #> 4731 yyxx251   C  51   Male     1     0         1        1          0 #> 1710 yyxx252   C  75   Male     1     1         3        1          0 #> 1201 yyxx253   C  69 Female     0     1         2        0          1 #> 1501 yyxx254   C  75 Female     1     1         4        0          1 #> 510  yyxx255   C  69   Male     0     1         4        1          0 #> 3361 yyxx256   C  62 Female     0     0         3        0          1 #> 3661 yyxx257   C  67 Female     0     1         3        0          1 #> 1371 yyxx258   C  71 Female     1     1         3        0         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Male     0     0         1        1          0 #> 3210 yyxx284   C  61   Male     0     0         4        1          0 #> 2571 yyxx285   C  50   Male     0     0         3        1          0 #> 3371 yyxx286   C  71   Male     0     0         4        1          0 #> 3801 yyxx287   C  73 Female     0     0         1        0          1 #> 3531 yyxx288   C  45 Female     0     0         3        0          1 #> 3951 yyxx289   C  60   Male     0     0         1        1          0 #> 1210  yyxx29   C  50 Female     0     0         1        0          1 #> 541  yyxx290   C  75 Female     1     1         3        0          1 #> 3351 yyxx291   C  75 Female     1     1         3        0          1 #> 1821 yyxx292   C  53   Male     0     0         2        1          0 #> 1421 yyxx293   C  72 Female     0     0         1        0          1 #> 4981 yyxx294   C  68 Female     1     0         1        0          1 #> 3131 yyxx295   C  75 Female     0     0         3        0          1 #> 1651 yyxx296   C  49 Female     0     0         1        0          1 #> 3711 yyxx297   C  68   Male     0     1         1        1          0 #> 2421 yyxx298   C  54 Female     0     1         3        0          1 #> 4451 yyxx299   C  56   Male     0     0         4        1          0 #> 4321   yyxx3   C  68 Female     1     0         1        0          1 #> 3541  yyxx30   C  49 Female     0     0         3        0          1 #> 4431 yyxx300   C  67 Female     0     1         3        0          1 #> 3501 yyxx301   C  72   Male     0     0         3        1          0 #> 3841 yyxx302   C  60   Male     1     1         4        1          0 #> 2771 yyxx303   C  65   Male     0     0         2        1          0 #> 3310 yyxx304   C  66   Male     1     0         3        1          0 #> 571  yyxx305   C  72 Female     0     0         3        0          1 #> 1621 yyxx306   C  63 Female     0     1         2        0          1 #> 2741 yyxx307   C  74 Female     1     0         1   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Male     0     1         2        1          0 #> 3411 yyxx407   C  69 Female     0     0         2        0          1 #> 4891 yyxx408   C  61 Female     1     0         3        0          1 #> 3561 yyxx409   C  56   Male     0     0         2        1          0 #> 1961  yyxx41   C  47 Female     1     1         1        0          1 #> 2310 yyxx410   C  53 Female     1     0         3        0          1 #> 4501 yyxx411   C  57 Female     0     1         4        0          1 #> 951  yyxx412   C  73 Female     1     0         3        0          1 #> 1510 yyxx413   C  57 Female     0     1         3        0          1 #> 821  yyxx414   C  47 Female     0     0         2        0          1 #> 1781 yyxx415   C  61 Female     1     0         4        0          1 #> 810  yyxx416   C  61   Male     1     0         1        1          0 #> 801  yyxx417   C  50 Female     0     1         1        0          1 #> 1910 yyxx418   C  57   Male     0     0         3        1          0 #> 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  4        1          0 #> 1091  yyxx68   C  57 Female     0     0         4        0          1 #> 2801  yyxx69   C  63 Female     0     1         4        0          1 #> 3301   yyxx7   C  48 Female     1     1         3        0          1 #> 2921  yyxx70   C  60 Female     1     1         1        0          1 #> 2441  yyxx71   C  74   Male     0     1         1        1          0 #> 3231  yyxx72   C  54   Male     1     0         4        1          0 #> 941   yyxx73   C  73   Male     0     1         1        1          0 #> 1571  yyxx74   C  58 Female     0     0         4        0          1 #> 4901  yyxx75   C  60 Female     0     1         3        0          1 #> 4061  yyxx76   C  52 Female     0     0         4        0          1 #> 1941  yyxx77   C  53   Male     0     0         4        1          0 #> 2901  yyxx78   C  48 Female     0     0         3        0          1 #> 2481  yyxx79   C  72 Female     1     0         3        0          1 #> 1921   yyxx8   C  74 Female     1     0         2        0          1 #> 1511  yyxx80   C  49 Female     1     1         3        0          1 #> 3110  yyxx81   C  49   Male     0     1         4        1          0 #> 2521  yyxx82   C  46   Male     0     0         1        1          0 #> 4161  yyxx83   C  54 Female     0     1         3        0          1 #> 3510  yyxx84   C  59 Female     1     1         2        0          1 #> 3241  yyxx85   C  56 Female     0     0         4        0          1 #> 3941  yyxx86   C  73 Female     0     0         4        0          1 #> 4210  yyxx87   C  47 Female     1     1         2        0          1 #> 1751  yyxx88   C  45 Female     1     1         3        0          1 #> 4411  yyxx89   C  64 Female     0     0         3        0          1 #> 2131   yyxx9   C  57 Female     0     1         4        0          1 #> 2351  yyxx90   C  53 Female     0     0         1        0          1 #> 4601  yyxx91   C  46   Male     1     0         4        1          0 #> 3671  yyxx92   C  55   Male     0     0         1        1          0 #> 2981  yyxx93   C  63   Male     0     0         4        1          0 #> 1191  yyxx94   C  71   Male     0     0         1        1          0 #> 2151  yyxx95   C  49 Female     0     0         4        0          1 #> 3071  yyxx96   C  61 Female     0     0         3        0          1 #> 3591  yyxx97   C  53   Male     1     1         2        1          0 #> 2391  yyxx98   C  72   Male     0     0         1        1          0 #> 1381  yyxx99   C  65   Male     0     0         1        1          0"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ess_footnote_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Note on Expected Sample Size Reduction — ess_footnote_text","title":"Note on Expected Sample Size Reduction — ess_footnote_text","text":"Note Expected Sample Size Reduction","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ess_footnote_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Note on Expected Sample Size Reduction — ess_footnote_text","text":"","code":"ess_footnote_text(width = 0.9 * getOption(\"width\"))"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ess_footnote_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Note on Expected Sample Size Reduction — ess_footnote_text","text":"width Number characters break string new lines (\\n).","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ess_footnote_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Note on Expected Sample Size Reduction — ess_footnote_text","text":"character string","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/estimate_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive individual weights in the matching step of MAIC — estimate_weights","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"Assuming data properly processed, function takes individual patient data (IPD) centered covariates (effect modifiers /prognostic variables) input, generates weights individual IPD trial match covariates aggregate data. plot function displays individuals weights key summary top right legend includes median weight, effective sample size (ESS), reduction percentage (percent ESS reduced original sample size). two options plotting: base R plot ggplot. default base R plot plot unscaled scaled separately. default ggplot plot unscaled scaled weights plot.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/estimate_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"","code":"estimate_weights(   data,   centered_colnames = NULL,   start_val = 0,   method = \"BFGS\",   n_boot_iteration = NULL,   set_seed_boot = 1234,   ... )  # S3 method for maicplus_estimate_weights plot(   x,   ggplot = FALSE,   bin_col = \"#6ECEB2\",   vline_col = \"#688CE8\",   main_title = NULL,   scaled_weights = TRUE,   bins = 50,   ... )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/estimate_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"data numeric matrix, centered covariates IPD, missing value cell allowed centered_colnames character numeric vector (column indicators) centered covariates start_val scalar, starting value coefficients propensity score regression method string, name optimization algorithm (see 'method' argument base::optim()) default \"BFGS\", options \"Nelder-Mead\", \"CG\", \"L-BFGS-B\", \"SANN\", \"Brent\" n_boot_iteration integer, number bootstrap iterations. default NULL means bootstrapping procedure triggered, hence element \"boot\" output list object NULL. set_seed_boot scalar, random seed conducting bootstrapping, relevant n_boot_iteration NULL. default, use seed 1234 ... Additional control parameters passed stats::optim. x object estimate_weights ggplot indicator print base weights plot ggplot weights plot bin_col string, color bins histogram vline_col string, color vertical line histogram main_title title plot. ggplot, name scaled weights plot unscaled weights plot, respectively. scaled_weights (base plot ) indicator using scaled weights instead regular weights bins (ggplot ) number bin parameter use","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/estimate_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"list following 4 elements, data data.frame, includes input data appended column 'weights' 'scaled_weights'. Scaled weights summation number rows data missing value effect modifiers centered_colnames column names centered effect modifiers data nr_missing number rows data least 1 missing value specified centered effect modifiers ess effective sample size, square sum divided sum squares opt R object returned base::optim(), assess convergence details boot n 2 k array NA, n equals number rows data, k equals n_boot_iteration. 2 columns second dimension include column numeric indexes rows data selected bootstrapping iteration column weights. boot NA argument n_boot_iteration set NULL","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/estimate_weights.html","id":"methods-by-generic-","dir":"Reference","previous_headings":"","what":"Methods (by generic)","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"plot(maicplus_estimate_weights): Plot method estimate_weights objects","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/estimate_weights.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"","code":"data(agd) data(adsl_sat) ipd_centered <- center_ipd(ipd = adsl_sat, agd = process_agd(agd)) centered_colnames <- grep(\"_CENTERED\", colnames(ipd_centered), value = TRUE) centered_colnames #> [1] \"AGE_CENTERED\"              \"AGE_MEDIAN_CENTERED\"       #> [3] \"AGE_SQUARED_CENTERED\"      \"SEX_MALE_CENTERED\"         #> [5] \"ECOG0_CENTERED\"            \"SMOKE_CENTERED\"            #> [7] \"N_PR_THER_MEDIAN_CENTERED\" weighted_data <- estimate_weights(data = ipd_centered, centered_colnames = centered_colnames) # \\donttest{ # To later estimate bootstrap confidence intervals, we calculate the weights # for the bootstrap samples: weighted_data_boot <- estimate_weights(   data = ipd_centered, centered_colnames = centered_colnames, n_boot_iteration = 500 ) # } plot(weighted_sat)   if (requireNamespace(\"ggplot2\")) {   plot(weighted_sat, ggplot = TRUE) } #> Loading required namespace: ggplot2"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ext_tte_transfer.html","id":null,"dir":"Reference","previous_headings":"","what":"helper function: transform TTE ADaM data to suitable input for survival R package — ext_tte_transfer","title":"helper function: transform TTE ADaM data to suitable input for survival R package — ext_tte_transfer","text":"helper function: transform TTE ADaM data suitable input survival R package","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ext_tte_transfer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"helper function: transform TTE ADaM data to suitable input for survival R package — ext_tte_transfer","text":"","code":"ext_tte_transfer(dd, time_scale = \"months\", trt = NULL)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ext_tte_transfer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"helper function: transform TTE ADaM data to suitable input for survival R package — ext_tte_transfer","text":"dd data frame, ADTTE read via haven::read_sas time_scale character string, 'years', 'months', 'weeks' 'days', time unit median survival time trt values include treatment column","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ext_tte_transfer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"helper function: transform TTE ADaM data to suitable input for survival R package — ext_tte_transfer","text":"data frame can used input survival::Surv","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/find_SE_from_CI.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","title":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","text":"Comparator studies often report confidence interval treatment effects. function calculates standard error treatment effect given reported confidence interval. relative treatment effect (.e. hazard ratio, odds ratio, risk ratio), function log confidence interval. risk difference mean difference, log confidence interval. option log confidence interval controlled 'logged' parameter.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/find_SE_from_CI.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","text":"","code":"find_SE_from_CI(   CI_lower = NULL,   CI_upper = NULL,   CI_perc = 0.95,   logged = TRUE )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/find_SE_from_CI.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","text":"CI_lower Reported lower percentile value treatment effect CI_upper Reported upper percentile value treatment effect CI_perc Percentage confidence interval reported logged Whether confidence interval logged. relative treatment effect, log applied estimated log treatment effect approximately normally distributed.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/find_SE_from_CI.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","text":"Standard error log relative treatment effect 'logged' true standard error treatment effect 'logged' false","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/find_SE_from_CI.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","text":"","code":"find_SE_from_CI(CI_lower = 0.55, CI_upper = 0.90, CI_perc = 0.95) #> [1] 0.1256341"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/generate_survival_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate time-to-event data — generate_survival_data","title":"Generate time-to-event data — generate_survival_data","text":"Function generates time--event data. follow data-generating mechanism Bender et al. Remiro-Azocar et al. 2021 simulate Weibull-distributed survival times proportional hazards parameterization. per Remiro-Azocar et al, set default scale parameter (lambda) 8.5 shape parameter (nu) 1.3 reflect frequently observed mortality trends metastatic cancer patients. Censoring times generated exponential distribution rate parameter 0.96 selected achieve censoring rate 35% active treatment baseline (values covariates set zero).","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/generate_survival_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate time-to-event data — generate_survival_data","text":"","code":"generate_survival_data(   N = 150,   ncov = 4,   lambda = 8.5,   nu = 1.3,   censor_rate = 0.96,   cor = 0.35,   marginal_mean = 0.6,   marginal_sd = 0.2,   beta = rep(-log(0.67), ncov),   gamma = sort(rep(c(0, -log(0.67)), length = ncov)),   d0 = log(0.25),   seed = 1 )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/generate_survival_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate time-to-event data — generate_survival_data","text":"N sample size ncov number covariates generate lambda scale parameter baseline hazard function nu shape parameter baseline hazard function censor_rate rate parameter exponential distribution censoring times cor correlation covariates marginal_mean marginal mean covariates marginal_sd marginal standard deviation covariates beta vector coefficients prognostic factors. Defaults -log(0.67) covariates. gamma vector coefficients effect modifiers. Default 0 half covariates -log(0.67) others. d0 coefficient treatment effect patients treat = 0 seed seed used simulation reproducibility","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/generate_survival_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate time-to-event data — generate_survival_data","text":"data.frame survival time, censor (event) status, treatment indicator, covariates","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/generate_survival_data.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generate time-to-event data — generate_survival_data","text":"Remiro-Azocar et al. 2021","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/get_pseudo_ipd_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","title":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","text":"Create pseudo IPD given aggregated binary data","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/get_pseudo_ipd_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","text":"","code":"get_pseudo_ipd_binary(binary_agd, format = c(\"stacked\", \"unstacked\"))"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/get_pseudo_ipd_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","text":"binary_agd data.frame take different formats depending format format string, \"stacked\" \"unstacked\"","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/get_pseudo_ipd_binary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","text":"data.frame pseudo binary IPD, columns USUBJID, ARM, RESPONSE","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/get_pseudo_ipd_binary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","text":"","code":"# example of unstacked testdat <- data.frame(Yes = 280, No = 120) rownames(testdat) <- \"B\" get_pseudo_ipd_binary(   binary_agd = testdat,   format = \"unstacked\" ) #>                    USUBJID ARM RESPONSE #> 1     pseudo_binary_subj_1   B     TRUE #> 2     pseudo_binary_subj_2   B     TRUE #> 3     pseudo_binary_subj_3   B     TRUE #> 4     pseudo_binary_subj_4   B     TRUE #> 5     pseudo_binary_subj_5   B     TRUE #> 6     pseudo_binary_subj_6   B     TRUE #> 7     pseudo_binary_subj_7   B     TRUE #> 8     pseudo_binary_subj_8   B     TRUE #> 9     pseudo_binary_subj_9   B     TRUE #> 10   pseudo_binary_subj_10   B     TRUE #> 11   pseudo_binary_subj_11   B     TRUE #> 12   pseudo_binary_subj_12   B     TRUE #> 13   pseudo_binary_subj_13   B     TRUE #> 14   pseudo_binary_subj_14   B     TRUE #> 15   pseudo_binary_subj_15   B     TRUE #> 16   pseudo_binary_subj_16   B     TRUE #> 17   pseudo_binary_subj_17   B     TRUE #> 18   pseudo_binary_subj_18   B     TRUE #> 19   pseudo_binary_subj_19   B     TRUE #> 20   pseudo_binary_subj_20   B     TRUE #> 21   pseudo_binary_subj_21   B     TRUE #> 22   pseudo_binary_subj_22   B     TRUE #> 23   pseudo_binary_subj_23   B     TRUE #> 24   pseudo_binary_subj_24   B     TRUE #> 25   pseudo_binary_subj_25   B     TRUE #> 26   pseudo_binary_subj_26   B     TRUE #> 27   pseudo_binary_subj_27   B     TRUE #> 28   pseudo_binary_subj_28   B     TRUE #> 29   pseudo_binary_subj_29   B     TRUE #> 30   pseudo_binary_subj_30   B     TRUE #> 31   pseudo_binary_subj_31   B     TRUE #> 32   pseudo_binary_subj_32   B     TRUE #> 33   pseudo_binary_subj_33   B     TRUE #> 34   pseudo_binary_subj_34   B     TRUE #> 35   pseudo_binary_subj_35   B     TRUE #> 36   pseudo_binary_subj_36   B     TRUE #> 37   pseudo_binary_subj_37   B     TRUE #> 38   pseudo_binary_subj_38   B     TRUE #> 39   pseudo_binary_subj_39   B     TRUE #> 40   pseudo_binary_subj_40   B     TRUE #> 41   pseudo_binary_subj_41   B     TRUE #> 42   pseudo_binary_subj_42   B     TRUE #> 43   pseudo_binary_subj_43   B     TRUE #> 44   pseudo_binary_subj_44   B     TRUE #> 45   pseudo_binary_subj_45   B     TRUE #> 46   pseudo_binary_subj_46   B     TRUE #> 47   pseudo_binary_subj_47   B     TRUE #> 48   pseudo_binary_subj_48   B     TRUE #> 49   pseudo_binary_subj_49   B     TRUE #> 50   pseudo_binary_subj_50   B     TRUE #> 51   pseudo_binary_subj_51   B     TRUE #> 52   pseudo_binary_subj_52   B     TRUE #> 53   pseudo_binary_subj_53   B     TRUE #> 54   pseudo_binary_subj_54   B     TRUE #> 55   pseudo_binary_subj_55   B     TRUE #> 56   pseudo_binary_subj_56   B     TRUE #> 57   pseudo_binary_subj_57   B     TRUE #> 58   pseudo_binary_subj_58   B     TRUE #> 59   pseudo_binary_subj_59   B     TRUE #> 60   pseudo_binary_subj_60   B     TRUE #> 61   pseudo_binary_subj_61   B     TRUE #> 62   pseudo_binary_subj_62   B     TRUE #> 63   pseudo_binary_subj_63   B     TRUE #> 64   pseudo_binary_subj_64   B     TRUE #> 65   pseudo_binary_subj_65   B     TRUE #> 66   pseudo_binary_subj_66   B     TRUE #> 67   pseudo_binary_subj_67   B     TRUE #> 68   pseudo_binary_subj_68   B     TRUE #> 69   pseudo_binary_subj_69   B     TRUE #> 70   pseudo_binary_subj_70   B     TRUE #> 71   pseudo_binary_subj_71   B     TRUE #> 72   pseudo_binary_subj_72   B     TRUE #> 73   pseudo_binary_subj_73   B     TRUE #> 74   pseudo_binary_subj_74   B     TRUE #> 75   pseudo_binary_subj_75   B     TRUE #> 76   pseudo_binary_subj_76   B     TRUE #> 77   pseudo_binary_subj_77   B     TRUE #> 78   pseudo_binary_subj_78   B     TRUE #> 79   pseudo_binary_subj_79   B     TRUE #> 80   pseudo_binary_subj_80   B     TRUE #> 81   pseudo_binary_subj_81   B     TRUE #> 82   pseudo_binary_subj_82   B     TRUE #> 83   pseudo_binary_subj_83   B     TRUE #> 84   pseudo_binary_subj_84   B     TRUE #> 85   pseudo_binary_subj_85   B     TRUE #> 86   pseudo_binary_subj_86   B     TRUE #> 87   pseudo_binary_subj_87   B     TRUE #> 88   pseudo_binary_subj_88   B     TRUE #> 89   pseudo_binary_subj_89   B     TRUE #> 90   pseudo_binary_subj_90   B     TRUE #> 91   pseudo_binary_subj_91   B     TRUE #> 92   pseudo_binary_subj_92   B     TRUE #> 93   pseudo_binary_subj_93   B     TRUE #> 94   pseudo_binary_subj_94   B     TRUE #> 95   pseudo_binary_subj_95   B     TRUE #> 96   pseudo_binary_subj_96   B     TRUE #> 97   pseudo_binary_subj_97   B     TRUE #> 98   pseudo_binary_subj_98   B     TRUE #> 99   pseudo_binary_subj_99   B     TRUE #> 100 pseudo_binary_subj_100   B     TRUE #> 101 pseudo_binary_subj_101   B     TRUE #> 102 pseudo_binary_subj_102   B     TRUE #> 103 pseudo_binary_subj_103   B     TRUE #> 104 pseudo_binary_subj_104   B     TRUE #> 105 pseudo_binary_subj_105   B     TRUE #> 106 pseudo_binary_subj_106   B     TRUE #> 107 pseudo_binary_subj_107   B     TRUE #> 108 pseudo_binary_subj_108   B     TRUE #> 109 pseudo_binary_subj_109   B     TRUE #> 110 pseudo_binary_subj_110   B     TRUE #> 111 pseudo_binary_subj_111   B     TRUE #> 112 pseudo_binary_subj_112   B     TRUE #> 113 pseudo_binary_subj_113   B     TRUE #> 114 pseudo_binary_subj_114   B     TRUE #> 115 pseudo_binary_subj_115   B     TRUE #> 116 pseudo_binary_subj_116   B     TRUE #> 117 pseudo_binary_subj_117   B     TRUE #> 118 pseudo_binary_subj_118   B     TRUE #> 119 pseudo_binary_subj_119   B     TRUE #> 120 pseudo_binary_subj_120   B     TRUE #> 121 pseudo_binary_subj_121   B     TRUE #> 122 pseudo_binary_subj_122   B     TRUE #> 123 pseudo_binary_subj_123   B     TRUE #> 124 pseudo_binary_subj_124   B     TRUE #> 125 pseudo_binary_subj_125   B     TRUE #> 126 pseudo_binary_subj_126   B     TRUE #> 127 pseudo_binary_subj_127   B     TRUE #> 128 pseudo_binary_subj_128   B     TRUE #> 129 pseudo_binary_subj_129   B     TRUE #> 130 pseudo_binary_subj_130   B     TRUE #> 131 pseudo_binary_subj_131   B     TRUE #> 132 pseudo_binary_subj_132   B     TRUE #> 133 pseudo_binary_subj_133   B     TRUE #> 134 pseudo_binary_subj_134   B     TRUE #> 135 pseudo_binary_subj_135   B     TRUE #> 136 pseudo_binary_subj_136   B     TRUE #> 137 pseudo_binary_subj_137   B     TRUE #> 138 pseudo_binary_subj_138   B     TRUE #> 139 pseudo_binary_subj_139   B     TRUE #> 140 pseudo_binary_subj_140   B     TRUE #> 141 pseudo_binary_subj_141   B     TRUE #> 142 pseudo_binary_subj_142   B     TRUE #> 143 pseudo_binary_subj_143   B     TRUE #> 144 pseudo_binary_subj_144   B     TRUE #> 145 pseudo_binary_subj_145   B     TRUE #> 146 pseudo_binary_subj_146   B     TRUE #> 147 pseudo_binary_subj_147   B     TRUE #> 148 pseudo_binary_subj_148   B     TRUE #> 149 pseudo_binary_subj_149   B     TRUE #> 150 pseudo_binary_subj_150   B     TRUE #> 151 pseudo_binary_subj_151   B     TRUE #> 152 pseudo_binary_subj_152   B     TRUE #> 153 pseudo_binary_subj_153   B     TRUE #> 154 pseudo_binary_subj_154   B     TRUE #> 155 pseudo_binary_subj_155   B     TRUE #> 156 pseudo_binary_subj_156   B     TRUE #> 157 pseudo_binary_subj_157   B     TRUE #> 158 pseudo_binary_subj_158   B     TRUE #> 159 pseudo_binary_subj_159   B     TRUE #> 160 pseudo_binary_subj_160   B     TRUE #> 161 pseudo_binary_subj_161   B     TRUE #> 162 pseudo_binary_subj_162   B     TRUE #> 163 pseudo_binary_subj_163   B     TRUE #> 164 pseudo_binary_subj_164   B     TRUE #> 165 pseudo_binary_subj_165   B     TRUE #> 166 pseudo_binary_subj_166   B     TRUE #> 167 pseudo_binary_subj_167   B     TRUE #> 168 pseudo_binary_subj_168   B     TRUE #> 169 pseudo_binary_subj_169   B     TRUE #> 170 pseudo_binary_subj_170   B     TRUE #> 171 pseudo_binary_subj_171   B     TRUE #> 172 pseudo_binary_subj_172   B     TRUE #> 173 pseudo_binary_subj_173   B     TRUE #> 174 pseudo_binary_subj_174   B     TRUE #> 175 pseudo_binary_subj_175   B     TRUE #> 176 pseudo_binary_subj_176   B     TRUE #> 177 pseudo_binary_subj_177   B     TRUE #> 178 pseudo_binary_subj_178   B     TRUE #> 179 pseudo_binary_subj_179   B     TRUE #> 180 pseudo_binary_subj_180   B     TRUE #> 181 pseudo_binary_subj_181   B     TRUE #> 182 pseudo_binary_subj_182   B     TRUE #> 183 pseudo_binary_subj_183   B     TRUE #> 184 pseudo_binary_subj_184   B     TRUE #> 185 pseudo_binary_subj_185   B     TRUE #> 186 pseudo_binary_subj_186   B     TRUE #> 187 pseudo_binary_subj_187   B     TRUE #> 188 pseudo_binary_subj_188   B     TRUE #> 189 pseudo_binary_subj_189   B     TRUE #> 190 pseudo_binary_subj_190   B     TRUE #> 191 pseudo_binary_subj_191   B     TRUE #> 192 pseudo_binary_subj_192   B     TRUE #> 193 pseudo_binary_subj_193   B     TRUE #> 194 pseudo_binary_subj_194   B     TRUE #> 195 pseudo_binary_subj_195   B     TRUE #> 196 pseudo_binary_subj_196   B     TRUE #> 197 pseudo_binary_subj_197   B     TRUE #> 198 pseudo_binary_subj_198   B     TRUE #> 199 pseudo_binary_subj_199   B     TRUE #> 200 pseudo_binary_subj_200   B     TRUE #> 201 pseudo_binary_subj_201   B     TRUE #> 202 pseudo_binary_subj_202   B     TRUE #> 203 pseudo_binary_subj_203   B     TRUE #> 204 pseudo_binary_subj_204   B     TRUE #> 205 pseudo_binary_subj_205   B     TRUE #> 206 pseudo_binary_subj_206   B     TRUE #> 207 pseudo_binary_subj_207   B     TRUE #> 208 pseudo_binary_subj_208   B     TRUE #> 209 pseudo_binary_subj_209   B     TRUE #> 210 pseudo_binary_subj_210   B     TRUE #> 211 pseudo_binary_subj_211   B     TRUE #> 212 pseudo_binary_subj_212   B     TRUE #> 213 pseudo_binary_subj_213   B     TRUE #> 214 pseudo_binary_subj_214   B     TRUE #> 215 pseudo_binary_subj_215   B     TRUE #> 216 pseudo_binary_subj_216   B     TRUE #> 217 pseudo_binary_subj_217   B     TRUE #> 218 pseudo_binary_subj_218   B     TRUE #> 219 pseudo_binary_subj_219   B     TRUE #> 220 pseudo_binary_subj_220   B     TRUE #> 221 pseudo_binary_subj_221   B     TRUE #> 222 pseudo_binary_subj_222   B     TRUE #> 223 pseudo_binary_subj_223   B     TRUE #> 224 pseudo_binary_subj_224   B     TRUE #> 225 pseudo_binary_subj_225   B     TRUE #> 226 pseudo_binary_subj_226   B     TRUE #> 227 pseudo_binary_subj_227   B     TRUE #> 228 pseudo_binary_subj_228   B     TRUE #> 229 pseudo_binary_subj_229   B     TRUE #> 230 pseudo_binary_subj_230   B     TRUE #> 231 pseudo_binary_subj_231   B     TRUE #> 232 pseudo_binary_subj_232   B     TRUE #> 233 pseudo_binary_subj_233   B     TRUE #> 234 pseudo_binary_subj_234   B     TRUE #> 235 pseudo_binary_subj_235   B     TRUE #> 236 pseudo_binary_subj_236   B     TRUE #> 237 pseudo_binary_subj_237   B     TRUE #> 238 pseudo_binary_subj_238   B     TRUE #> 239 pseudo_binary_subj_239   B     TRUE #> 240 pseudo_binary_subj_240   B     TRUE #> 241 pseudo_binary_subj_241   B     TRUE #> 242 pseudo_binary_subj_242   B     TRUE #> 243 pseudo_binary_subj_243   B     TRUE #> 244 pseudo_binary_subj_244   B     TRUE #> 245 pseudo_binary_subj_245   B     TRUE #> 246 pseudo_binary_subj_246   B     TRUE #> 247 pseudo_binary_subj_247   B     TRUE #> 248 pseudo_binary_subj_248   B     TRUE #> 249 pseudo_binary_subj_249   B     TRUE #> 250 pseudo_binary_subj_250   B     TRUE #> 251 pseudo_binary_subj_251   B     TRUE #> 252 pseudo_binary_subj_252   B     TRUE #> 253 pseudo_binary_subj_253   B     TRUE #> 254 pseudo_binary_subj_254   B     TRUE #> 255 pseudo_binary_subj_255   B     TRUE #> 256 pseudo_binary_subj_256   B     TRUE #> 257 pseudo_binary_subj_257   B     TRUE #> 258 pseudo_binary_subj_258   B     TRUE #> 259 pseudo_binary_subj_259   B     TRUE #> 260 pseudo_binary_subj_260   B     TRUE #> 261 pseudo_binary_subj_261   B     TRUE #> 262 pseudo_binary_subj_262   B     TRUE #> 263 pseudo_binary_subj_263   B     TRUE #> 264 pseudo_binary_subj_264   B     TRUE #> 265 pseudo_binary_subj_265   B     TRUE #> 266 pseudo_binary_subj_266   B     TRUE #> 267 pseudo_binary_subj_267   B     TRUE #> 268 pseudo_binary_subj_268   B     TRUE #> 269 pseudo_binary_subj_269   B     TRUE #> 270 pseudo_binary_subj_270   B     TRUE #> 271 pseudo_binary_subj_271   B     TRUE #> 272 pseudo_binary_subj_272   B     TRUE #> 273 pseudo_binary_subj_273   B     TRUE #> 274 pseudo_binary_subj_274   B     TRUE #> 275 pseudo_binary_subj_275   B     TRUE #> 276 pseudo_binary_subj_276   B     TRUE #> 277 pseudo_binary_subj_277   B     TRUE #> 278 pseudo_binary_subj_278   B     TRUE #> 279 pseudo_binary_subj_279   B     TRUE #> 280 pseudo_binary_subj_280   B     TRUE #> 281 pseudo_binary_subj_281   B    FALSE #> 282 pseudo_binary_subj_282   B    FALSE #> 283 pseudo_binary_subj_283   B    FALSE #> 284 pseudo_binary_subj_284   B    FALSE #> 285 pseudo_binary_subj_285   B    FALSE #> 286 pseudo_binary_subj_286   B    FALSE #> 287 pseudo_binary_subj_287   B    FALSE #> 288 pseudo_binary_subj_288   B    FALSE #> 289 pseudo_binary_subj_289   B    FALSE #> 290 pseudo_binary_subj_290   B    FALSE #> 291 pseudo_binary_subj_291   B    FALSE #> 292 pseudo_binary_subj_292   B    FALSE #> 293 pseudo_binary_subj_293   B    FALSE #> 294 pseudo_binary_subj_294   B    FALSE #> 295 pseudo_binary_subj_295   B    FALSE #> 296 pseudo_binary_subj_296   B    FALSE #> 297 pseudo_binary_subj_297   B    FALSE #> 298 pseudo_binary_subj_298   B    FALSE #> 299 pseudo_binary_subj_299   B    FALSE #> 300 pseudo_binary_subj_300   B    FALSE #> 301 pseudo_binary_subj_301   B    FALSE #> 302 pseudo_binary_subj_302   B    FALSE #> 303 pseudo_binary_subj_303   B    FALSE #> 304 pseudo_binary_subj_304   B    FALSE #> 305 pseudo_binary_subj_305   B    FALSE #> 306 pseudo_binary_subj_306   B    FALSE #> 307 pseudo_binary_subj_307   B    FALSE #> 308 pseudo_binary_subj_308   B    FALSE #> 309 pseudo_binary_subj_309   B    FALSE #> 310 pseudo_binary_subj_310   B    FALSE #> 311 pseudo_binary_subj_311   B    FALSE #> 312 pseudo_binary_subj_312   B    FALSE #> 313 pseudo_binary_subj_313   B    FALSE #> 314 pseudo_binary_subj_314   B    FALSE #> 315 pseudo_binary_subj_315   B    FALSE #> 316 pseudo_binary_subj_316   B    FALSE #> 317 pseudo_binary_subj_317   B    FALSE #> 318 pseudo_binary_subj_318   B    FALSE #> 319 pseudo_binary_subj_319   B    FALSE #> 320 pseudo_binary_subj_320   B    FALSE #> 321 pseudo_binary_subj_321   B    FALSE #> 322 pseudo_binary_subj_322   B    FALSE #> 323 pseudo_binary_subj_323   B    FALSE #> 324 pseudo_binary_subj_324   B    FALSE #> 325 pseudo_binary_subj_325   B    FALSE #> 326 pseudo_binary_subj_326   B    FALSE #> 327 pseudo_binary_subj_327   B    FALSE #> 328 pseudo_binary_subj_328   B    FALSE #> 329 pseudo_binary_subj_329   B    FALSE #> 330 pseudo_binary_subj_330   B    FALSE #> 331 pseudo_binary_subj_331   B    FALSE #> 332 pseudo_binary_subj_332   B    FALSE #> 333 pseudo_binary_subj_333   B    FALSE #> 334 pseudo_binary_subj_334   B    FALSE #> 335 pseudo_binary_subj_335   B    FALSE #> 336 pseudo_binary_subj_336   B    FALSE #> 337 pseudo_binary_subj_337   B    FALSE #> 338 pseudo_binary_subj_338   B    FALSE #> 339 pseudo_binary_subj_339   B    FALSE #> 340 pseudo_binary_subj_340   B    FALSE #> 341 pseudo_binary_subj_341   B    FALSE #> 342 pseudo_binary_subj_342   B    FALSE #> 343 pseudo_binary_subj_343   B    FALSE #> 344 pseudo_binary_subj_344   B    FALSE #> 345 pseudo_binary_subj_345   B    FALSE #> 346 pseudo_binary_subj_346   B    FALSE #> 347 pseudo_binary_subj_347   B    FALSE #> 348 pseudo_binary_subj_348   B    FALSE #> 349 pseudo_binary_subj_349   B    FALSE #> 350 pseudo_binary_subj_350   B    FALSE #> 351 pseudo_binary_subj_351   B    FALSE #> 352 pseudo_binary_subj_352   B    FALSE #> 353 pseudo_binary_subj_353   B    FALSE #> 354 pseudo_binary_subj_354   B    FALSE #> 355 pseudo_binary_subj_355   B    FALSE #> 356 pseudo_binary_subj_356   B    FALSE #> 357 pseudo_binary_subj_357   B    FALSE #> 358 pseudo_binary_subj_358   B    FALSE #> 359 pseudo_binary_subj_359   B    FALSE #> 360 pseudo_binary_subj_360   B    FALSE #> 361 pseudo_binary_subj_361   B    FALSE #> 362 pseudo_binary_subj_362   B    FALSE #> 363 pseudo_binary_subj_363   B    FALSE #> 364 pseudo_binary_subj_364   B    FALSE #> 365 pseudo_binary_subj_365   B    FALSE #> 366 pseudo_binary_subj_366   B    FALSE #> 367 pseudo_binary_subj_367   B    FALSE #> 368 pseudo_binary_subj_368   B    FALSE #> 369 pseudo_binary_subj_369   B    FALSE #> 370 pseudo_binary_subj_370   B    FALSE #> 371 pseudo_binary_subj_371   B    FALSE #> 372 pseudo_binary_subj_372   B    FALSE #> 373 pseudo_binary_subj_373   B    FALSE #> 374 pseudo_binary_subj_374   B    FALSE #> 375 pseudo_binary_subj_375   B    FALSE #> 376 pseudo_binary_subj_376   B    FALSE #> 377 pseudo_binary_subj_377   B    FALSE #> 378 pseudo_binary_subj_378   B    FALSE #> 379 pseudo_binary_subj_379   B    FALSE #> 380 pseudo_binary_subj_380   B    FALSE #> 381 pseudo_binary_subj_381   B    FALSE #> 382 pseudo_binary_subj_382   B    FALSE #> 383 pseudo_binary_subj_383   B    FALSE #> 384 pseudo_binary_subj_384   B    FALSE #> 385 pseudo_binary_subj_385   B    FALSE #> 386 pseudo_binary_subj_386   B    FALSE #> 387 pseudo_binary_subj_387   B    FALSE #> 388 pseudo_binary_subj_388   B    FALSE #> 389 pseudo_binary_subj_389   B    FALSE #> 390 pseudo_binary_subj_390   B    FALSE #> 391 pseudo_binary_subj_391   B    FALSE #> 392 pseudo_binary_subj_392   B    FALSE #> 393 pseudo_binary_subj_393   B    FALSE #> 394 pseudo_binary_subj_394   B    FALSE #> 395 pseudo_binary_subj_395   B    FALSE #> 396 pseudo_binary_subj_396   B    FALSE #> 397 pseudo_binary_subj_397   B    FALSE #> 398 pseudo_binary_subj_398   B    FALSE #> 399 pseudo_binary_subj_399   B    FALSE #> 400 pseudo_binary_subj_400   B    FALSE  # example of stacked get_pseudo_ipd_binary(   binary_agd = data.frame(     ARM = rep(\"B\", 2),     RESPONSE = c(\"YES\", \"NO\"),     COUNT = c(280, 120)   ),   format = \"stacked\" ) #>                    USUBJID ARM RESPONSE #> 1     pseudo_binary_subj_1   B     TRUE #> 2     pseudo_binary_subj_2   B     TRUE #> 3     pseudo_binary_subj_3   B     TRUE #> 4     pseudo_binary_subj_4   B     TRUE #> 5     pseudo_binary_subj_5   B     TRUE #> 6     pseudo_binary_subj_6   B     TRUE #> 7     pseudo_binary_subj_7   B     TRUE #> 8     pseudo_binary_subj_8   B     TRUE #> 9     pseudo_binary_subj_9   B     TRUE #> 10   pseudo_binary_subj_10   B     TRUE #> 11   pseudo_binary_subj_11   B     TRUE #> 12   pseudo_binary_subj_12   B     TRUE #> 13   pseudo_binary_subj_13   B     TRUE #> 14   pseudo_binary_subj_14   B     TRUE #> 15   pseudo_binary_subj_15   B     TRUE #> 16   pseudo_binary_subj_16   B     TRUE #> 17   pseudo_binary_subj_17   B     TRUE #> 18   pseudo_binary_subj_18   B     TRUE #> 19   pseudo_binary_subj_19   B     TRUE #> 20   pseudo_binary_subj_20   B     TRUE #> 21   pseudo_binary_subj_21   B     TRUE #> 22   pseudo_binary_subj_22   B     TRUE #> 23   pseudo_binary_subj_23   B     TRUE #> 24   pseudo_binary_subj_24   B     TRUE #> 25   pseudo_binary_subj_25   B     TRUE #> 26   pseudo_binary_subj_26   B     TRUE #> 27   pseudo_binary_subj_27   B     TRUE #> 28   pseudo_binary_subj_28   B     TRUE #> 29   pseudo_binary_subj_29   B     TRUE #> 30   pseudo_binary_subj_30   B     TRUE #> 31   pseudo_binary_subj_31   B     TRUE #> 32   pseudo_binary_subj_32   B     TRUE #> 33   pseudo_binary_subj_33   B     TRUE #> 34   pseudo_binary_subj_34   B     TRUE #> 35   pseudo_binary_subj_35   B     TRUE #> 36   pseudo_binary_subj_36   B     TRUE #> 37   pseudo_binary_subj_37   B     TRUE #> 38   pseudo_binary_subj_38   B     TRUE #> 39   pseudo_binary_subj_39   B     TRUE #> 40   pseudo_binary_subj_40   B     TRUE #> 41   pseudo_binary_subj_41   B     TRUE #> 42   pseudo_binary_subj_42   B     TRUE #> 43   pseudo_binary_subj_43   B     TRUE #> 44   pseudo_binary_subj_44   B     TRUE #> 45   pseudo_binary_subj_45   B     TRUE #> 46   pseudo_binary_subj_46   B     TRUE #> 47   pseudo_binary_subj_47   B     TRUE #> 48   pseudo_binary_subj_48   B     TRUE #> 49   pseudo_binary_subj_49   B     TRUE #> 50   pseudo_binary_subj_50   B     TRUE #> 51   pseudo_binary_subj_51   B     TRUE #> 52   pseudo_binary_subj_52   B     TRUE #> 53   pseudo_binary_subj_53   B     TRUE #> 54   pseudo_binary_subj_54   B     TRUE #> 55   pseudo_binary_subj_55   B     TRUE #> 56   pseudo_binary_subj_56   B     TRUE #> 57   pseudo_binary_subj_57   B     TRUE #> 58   pseudo_binary_subj_58   B     TRUE #> 59   pseudo_binary_subj_59   B     TRUE #> 60   pseudo_binary_subj_60   B     TRUE #> 61   pseudo_binary_subj_61   B     TRUE #> 62   pseudo_binary_subj_62   B     TRUE #> 63   pseudo_binary_subj_63   B     TRUE #> 64   pseudo_binary_subj_64   B     TRUE #> 65   pseudo_binary_subj_65   B     TRUE #> 66   pseudo_binary_subj_66   B     TRUE #> 67   pseudo_binary_subj_67   B     TRUE #> 68   pseudo_binary_subj_68   B     TRUE #> 69   pseudo_binary_subj_69   B     TRUE #> 70   pseudo_binary_subj_70   B     TRUE #> 71   pseudo_binary_subj_71   B     TRUE #> 72   pseudo_binary_subj_72   B     TRUE #> 73   pseudo_binary_subj_73   B     TRUE #> 74   pseudo_binary_subj_74   B     TRUE #> 75   pseudo_binary_subj_75   B     TRUE #> 76   pseudo_binary_subj_76   B     TRUE #> 77   pseudo_binary_subj_77   B     TRUE #> 78   pseudo_binary_subj_78   B     TRUE #> 79   pseudo_binary_subj_79   B     TRUE #> 80   pseudo_binary_subj_80   B     TRUE #> 81   pseudo_binary_subj_81   B     TRUE #> 82   pseudo_binary_subj_82   B     TRUE #> 83   pseudo_binary_subj_83   B     TRUE #> 84   pseudo_binary_subj_84   B     TRUE #> 85   pseudo_binary_subj_85   B     TRUE #> 86   pseudo_binary_subj_86   B     TRUE #> 87   pseudo_binary_subj_87   B     TRUE #> 88   pseudo_binary_subj_88   B     TRUE #> 89   pseudo_binary_subj_89   B     TRUE #> 90   pseudo_binary_subj_90   B     TRUE #> 91   pseudo_binary_subj_91   B     TRUE #> 92   pseudo_binary_subj_92   B     TRUE #> 93   pseudo_binary_subj_93   B     TRUE #> 94   pseudo_binary_subj_94   B     TRUE #> 95   pseudo_binary_subj_95   B     TRUE #> 96   pseudo_binary_subj_96   B     TRUE #> 97   pseudo_binary_subj_97   B     TRUE #> 98   pseudo_binary_subj_98   B     TRUE #> 99   pseudo_binary_subj_99   B     TRUE #> 100 pseudo_binary_subj_100   B     TRUE #> 101 pseudo_binary_subj_101   B     TRUE #> 102 pseudo_binary_subj_102   B     TRUE #> 103 pseudo_binary_subj_103   B     TRUE #> 104 pseudo_binary_subj_104   B     TRUE #> 105 pseudo_binary_subj_105   B     TRUE #> 106 pseudo_binary_subj_106   B     TRUE #> 107 pseudo_binary_subj_107   B     TRUE #> 108 pseudo_binary_subj_108   B     TRUE #> 109 pseudo_binary_subj_109   B     TRUE #> 110 pseudo_binary_subj_110   B     TRUE #> 111 pseudo_binary_subj_111   B     TRUE #> 112 pseudo_binary_subj_112   B     TRUE #> 113 pseudo_binary_subj_113   B     TRUE #> 114 pseudo_binary_subj_114   B     TRUE #> 115 pseudo_binary_subj_115   B     TRUE #> 116 pseudo_binary_subj_116   B     TRUE #> 117 pseudo_binary_subj_117   B     TRUE #> 118 pseudo_binary_subj_118   B     TRUE #> 119 pseudo_binary_subj_119   B     TRUE #> 120 pseudo_binary_subj_120   B     TRUE #> 121 pseudo_binary_subj_121   B     TRUE #> 122 pseudo_binary_subj_122   B     TRUE #> 123 pseudo_binary_subj_123   B     TRUE #> 124 pseudo_binary_subj_124   B     TRUE #> 125 pseudo_binary_subj_125   B     TRUE #> 126 pseudo_binary_subj_126   B     TRUE #> 127 pseudo_binary_subj_127   B     TRUE #> 128 pseudo_binary_subj_128   B     TRUE #> 129 pseudo_binary_subj_129   B     TRUE #> 130 pseudo_binary_subj_130   B     TRUE #> 131 pseudo_binary_subj_131   B     TRUE #> 132 pseudo_binary_subj_132   B     TRUE #> 133 pseudo_binary_subj_133   B     TRUE #> 134 pseudo_binary_subj_134   B     TRUE #> 135 pseudo_binary_subj_135   B     TRUE #> 136 pseudo_binary_subj_136   B     TRUE #> 137 pseudo_binary_subj_137   B     TRUE #> 138 pseudo_binary_subj_138   B     TRUE #> 139 pseudo_binary_subj_139   B     TRUE #> 140 pseudo_binary_subj_140   B     TRUE #> 141 pseudo_binary_subj_141   B     TRUE #> 142 pseudo_binary_subj_142   B     TRUE #> 143 pseudo_binary_subj_143   B     TRUE #> 144 pseudo_binary_subj_144   B     TRUE #> 145 pseudo_binary_subj_145   B     TRUE #> 146 pseudo_binary_subj_146   B     TRUE #> 147 pseudo_binary_subj_147   B     TRUE #> 148 pseudo_binary_subj_148   B     TRUE #> 149 pseudo_binary_subj_149   B     TRUE #> 150 pseudo_binary_subj_150   B     TRUE #> 151 pseudo_binary_subj_151   B     TRUE #> 152 pseudo_binary_subj_152   B     TRUE #> 153 pseudo_binary_subj_153   B     TRUE #> 154 pseudo_binary_subj_154   B     TRUE #> 155 pseudo_binary_subj_155   B     TRUE #> 156 pseudo_binary_subj_156   B     TRUE #> 157 pseudo_binary_subj_157   B     TRUE #> 158 pseudo_binary_subj_158   B     TRUE #> 159 pseudo_binary_subj_159   B     TRUE #> 160 pseudo_binary_subj_160   B     TRUE #> 161 pseudo_binary_subj_161   B     TRUE #> 162 pseudo_binary_subj_162   B     TRUE #> 163 pseudo_binary_subj_163   B     TRUE #> 164 pseudo_binary_subj_164   B     TRUE #> 165 pseudo_binary_subj_165   B     TRUE #> 166 pseudo_binary_subj_166   B     TRUE #> 167 pseudo_binary_subj_167   B     TRUE #> 168 pseudo_binary_subj_168   B     TRUE #> 169 pseudo_binary_subj_169   B     TRUE #> 170 pseudo_binary_subj_170   B     TRUE #> 171 pseudo_binary_subj_171   B     TRUE #> 172 pseudo_binary_subj_172   B     TRUE #> 173 pseudo_binary_subj_173   B     TRUE #> 174 pseudo_binary_subj_174   B     TRUE #> 175 pseudo_binary_subj_175   B     TRUE #> 176 pseudo_binary_subj_176   B     TRUE #> 177 pseudo_binary_subj_177   B     TRUE #> 178 pseudo_binary_subj_178   B     TRUE #> 179 pseudo_binary_subj_179   B     TRUE #> 180 pseudo_binary_subj_180   B     TRUE #> 181 pseudo_binary_subj_181   B     TRUE #> 182 pseudo_binary_subj_182   B     TRUE #> 183 pseudo_binary_subj_183   B     TRUE #> 184 pseudo_binary_subj_184   B     TRUE #> 185 pseudo_binary_subj_185   B     TRUE #> 186 pseudo_binary_subj_186   B     TRUE #> 187 pseudo_binary_subj_187   B     TRUE #> 188 pseudo_binary_subj_188   B     TRUE #> 189 pseudo_binary_subj_189   B     TRUE #> 190 pseudo_binary_subj_190   B     TRUE #> 191 pseudo_binary_subj_191   B     TRUE #> 192 pseudo_binary_subj_192   B     TRUE #> 193 pseudo_binary_subj_193   B     TRUE #> 194 pseudo_binary_subj_194   B     TRUE #> 195 pseudo_binary_subj_195   B     TRUE #> 196 pseudo_binary_subj_196   B     TRUE #> 197 pseudo_binary_subj_197   B     TRUE #> 198 pseudo_binary_subj_198   B     TRUE #> 199 pseudo_binary_subj_199   B     TRUE #> 200 pseudo_binary_subj_200   B     TRUE #> 201 pseudo_binary_subj_201   B     TRUE #> 202 pseudo_binary_subj_202   B     TRUE #> 203 pseudo_binary_subj_203   B     TRUE #> 204 pseudo_binary_subj_204   B     TRUE #> 205 pseudo_binary_subj_205   B     TRUE #> 206 pseudo_binary_subj_206   B     TRUE #> 207 pseudo_binary_subj_207   B     TRUE #> 208 pseudo_binary_subj_208   B     TRUE #> 209 pseudo_binary_subj_209   B     TRUE #> 210 pseudo_binary_subj_210   B     TRUE #> 211 pseudo_binary_subj_211   B     TRUE #> 212 pseudo_binary_subj_212   B     TRUE #> 213 pseudo_binary_subj_213   B     TRUE #> 214 pseudo_binary_subj_214   B     TRUE #> 215 pseudo_binary_subj_215   B     TRUE #> 216 pseudo_binary_subj_216   B     TRUE #> 217 pseudo_binary_subj_217   B     TRUE #> 218 pseudo_binary_subj_218   B     TRUE #> 219 pseudo_binary_subj_219   B     TRUE #> 220 pseudo_binary_subj_220   B     TRUE #> 221 pseudo_binary_subj_221   B     TRUE #> 222 pseudo_binary_subj_222   B     TRUE #> 223 pseudo_binary_subj_223   B     TRUE #> 224 pseudo_binary_subj_224   B     TRUE #> 225 pseudo_binary_subj_225   B     TRUE #> 226 pseudo_binary_subj_226   B     TRUE #> 227 pseudo_binary_subj_227   B     TRUE #> 228 pseudo_binary_subj_228   B     TRUE #> 229 pseudo_binary_subj_229   B     TRUE #> 230 pseudo_binary_subj_230   B     TRUE #> 231 pseudo_binary_subj_231   B     TRUE #> 232 pseudo_binary_subj_232   B     TRUE #> 233 pseudo_binary_subj_233   B     TRUE #> 234 pseudo_binary_subj_234   B     TRUE #> 235 pseudo_binary_subj_235   B     TRUE #> 236 pseudo_binary_subj_236   B     TRUE #> 237 pseudo_binary_subj_237   B     TRUE #> 238 pseudo_binary_subj_238   B     TRUE #> 239 pseudo_binary_subj_239   B     TRUE #> 240 pseudo_binary_subj_240   B     TRUE #> 241 pseudo_binary_subj_241   B     TRUE #> 242 pseudo_binary_subj_242   B     TRUE #> 243 pseudo_binary_subj_243   B     TRUE #> 244 pseudo_binary_subj_244   B     TRUE #> 245 pseudo_binary_subj_245   B     TRUE #> 246 pseudo_binary_subj_246   B     TRUE #> 247 pseudo_binary_subj_247   B     TRUE #> 248 pseudo_binary_subj_248   B     TRUE #> 249 pseudo_binary_subj_249   B     TRUE #> 250 pseudo_binary_subj_250   B     TRUE #> 251 pseudo_binary_subj_251   B     TRUE #> 252 pseudo_binary_subj_252   B     TRUE #> 253 pseudo_binary_subj_253   B     TRUE #> 254 pseudo_binary_subj_254   B     TRUE #> 255 pseudo_binary_subj_255   B     TRUE #> 256 pseudo_binary_subj_256   B     TRUE #> 257 pseudo_binary_subj_257   B     TRUE #> 258 pseudo_binary_subj_258   B     TRUE #> 259 pseudo_binary_subj_259   B     TRUE #> 260 pseudo_binary_subj_260   B     TRUE #> 261 pseudo_binary_subj_261   B     TRUE #> 262 pseudo_binary_subj_262   B     TRUE #> 263 pseudo_binary_subj_263   B     TRUE #> 264 pseudo_binary_subj_264   B     TRUE #> 265 pseudo_binary_subj_265   B     TRUE #> 266 pseudo_binary_subj_266   B     TRUE #> 267 pseudo_binary_subj_267   B     TRUE #> 268 pseudo_binary_subj_268   B     TRUE #> 269 pseudo_binary_subj_269   B     TRUE #> 270 pseudo_binary_subj_270   B     TRUE #> 271 pseudo_binary_subj_271   B     TRUE #> 272 pseudo_binary_subj_272   B     TRUE #> 273 pseudo_binary_subj_273   B     TRUE #> 274 pseudo_binary_subj_274   B     TRUE #> 275 pseudo_binary_subj_275   B     TRUE #> 276 pseudo_binary_subj_276   B     TRUE #> 277 pseudo_binary_subj_277   B     TRUE #> 278 pseudo_binary_subj_278   B     TRUE #> 279 pseudo_binary_subj_279   B     TRUE #> 280 pseudo_binary_subj_280   B     TRUE #> 281 pseudo_binary_subj_281   B    FALSE #> 282 pseudo_binary_subj_282   B    FALSE #> 283 pseudo_binary_subj_283   B    FALSE #> 284 pseudo_binary_subj_284   B    FALSE #> 285 pseudo_binary_subj_285   B    FALSE #> 286 pseudo_binary_subj_286   B    FALSE #> 287 pseudo_binary_subj_287   B    FALSE #> 288 pseudo_binary_subj_288   B    FALSE #> 289 pseudo_binary_subj_289   B    FALSE #> 290 pseudo_binary_subj_290   B    FALSE #> 291 pseudo_binary_subj_291   B    FALSE #> 292 pseudo_binary_subj_292   B    FALSE #> 293 pseudo_binary_subj_293   B    FALSE #> 294 pseudo_binary_subj_294   B    FALSE #> 295 pseudo_binary_subj_295   B    FALSE #> 296 pseudo_binary_subj_296   B    FALSE #> 297 pseudo_binary_subj_297   B    FALSE #> 298 pseudo_binary_subj_298   B    FALSE #> 299 pseudo_binary_subj_299   B    FALSE #> 300 pseudo_binary_subj_300   B    FALSE #> 301 pseudo_binary_subj_301   B    FALSE #> 302 pseudo_binary_subj_302   B    FALSE #> 303 pseudo_binary_subj_303   B    FALSE #> 304 pseudo_binary_subj_304   B    FALSE #> 305 pseudo_binary_subj_305   B    FALSE #> 306 pseudo_binary_subj_306   B    FALSE #> 307 pseudo_binary_subj_307   B    FALSE #> 308 pseudo_binary_subj_308   B    FALSE #> 309 pseudo_binary_subj_309   B    FALSE #> 310 pseudo_binary_subj_310   B    FALSE #> 311 pseudo_binary_subj_311   B    FALSE #> 312 pseudo_binary_subj_312   B    FALSE #> 313 pseudo_binary_subj_313   B    FALSE #> 314 pseudo_binary_subj_314   B    FALSE #> 315 pseudo_binary_subj_315   B    FALSE #> 316 pseudo_binary_subj_316   B    FALSE #> 317 pseudo_binary_subj_317   B    FALSE #> 318 pseudo_binary_subj_318   B    FALSE #> 319 pseudo_binary_subj_319   B    FALSE #> 320 pseudo_binary_subj_320   B    FALSE #> 321 pseudo_binary_subj_321   B    FALSE #> 322 pseudo_binary_subj_322   B    FALSE #> 323 pseudo_binary_subj_323   B    FALSE #> 324 pseudo_binary_subj_324   B    FALSE #> 325 pseudo_binary_subj_325   B    FALSE #> 326 pseudo_binary_subj_326   B    FALSE #> 327 pseudo_binary_subj_327   B    FALSE #> 328 pseudo_binary_subj_328   B    FALSE #> 329 pseudo_binary_subj_329   B    FALSE #> 330 pseudo_binary_subj_330   B    FALSE #> 331 pseudo_binary_subj_331   B    FALSE #> 332 pseudo_binary_subj_332   B    FALSE #> 333 pseudo_binary_subj_333   B    FALSE #> 334 pseudo_binary_subj_334   B    FALSE #> 335 pseudo_binary_subj_335   B    FALSE #> 336 pseudo_binary_subj_336   B    FALSE #> 337 pseudo_binary_subj_337   B    FALSE #> 338 pseudo_binary_subj_338   B    FALSE #> 339 pseudo_binary_subj_339   B    FALSE #> 340 pseudo_binary_subj_340   B    FALSE #> 341 pseudo_binary_subj_341   B    FALSE #> 342 pseudo_binary_subj_342   B    FALSE #> 343 pseudo_binary_subj_343   B    FALSE #> 344 pseudo_binary_subj_344   B    FALSE #> 345 pseudo_binary_subj_345   B    FALSE #> 346 pseudo_binary_subj_346   B    FALSE #> 347 pseudo_binary_subj_347   B    FALSE #> 348 pseudo_binary_subj_348   B    FALSE #> 349 pseudo_binary_subj_349   B    FALSE #> 350 pseudo_binary_subj_350   B    FALSE #> 351 pseudo_binary_subj_351   B    FALSE #> 352 pseudo_binary_subj_352   B    FALSE #> 353 pseudo_binary_subj_353   B    FALSE #> 354 pseudo_binary_subj_354   B    FALSE #> 355 pseudo_binary_subj_355   B    FALSE #> 356 pseudo_binary_subj_356   B    FALSE #> 357 pseudo_binary_subj_357   B    FALSE #> 358 pseudo_binary_subj_358   B    FALSE #> 359 pseudo_binary_subj_359   B    FALSE #> 360 pseudo_binary_subj_360   B    FALSE #> 361 pseudo_binary_subj_361   B    FALSE #> 362 pseudo_binary_subj_362   B    FALSE #> 363 pseudo_binary_subj_363   B    FALSE #> 364 pseudo_binary_subj_364   B    FALSE #> 365 pseudo_binary_subj_365   B    FALSE #> 366 pseudo_binary_subj_366   B    FALSE #> 367 pseudo_binary_subj_367   B    FALSE #> 368 pseudo_binary_subj_368   B    FALSE #> 369 pseudo_binary_subj_369   B    FALSE #> 370 pseudo_binary_subj_370   B    FALSE #> 371 pseudo_binary_subj_371   B    FALSE #> 372 pseudo_binary_subj_372   B    FALSE #> 373 pseudo_binary_subj_373   B    FALSE #> 374 pseudo_binary_subj_374   B    FALSE #> 375 pseudo_binary_subj_375   B    FALSE #> 376 pseudo_binary_subj_376   B    FALSE #> 377 pseudo_binary_subj_377   B    FALSE #> 378 pseudo_binary_subj_378   B    FALSE #> 379 pseudo_binary_subj_379   B    FALSE #> 380 pseudo_binary_subj_380   B    FALSE #> 381 pseudo_binary_subj_381   B    FALSE #> 382 pseudo_binary_subj_382   B    FALSE #> 383 pseudo_binary_subj_383   B    FALSE #> 384 pseudo_binary_subj_384   B    FALSE #> 385 pseudo_binary_subj_385   B    FALSE #> 386 pseudo_binary_subj_386   B    FALSE #> 387 pseudo_binary_subj_387   B    FALSE #> 388 pseudo_binary_subj_388   B    FALSE #> 389 pseudo_binary_subj_389   B    FALSE #> 390 pseudo_binary_subj_390   B    FALSE #> 391 pseudo_binary_subj_391   B    FALSE #> 392 pseudo_binary_subj_392   B    FALSE #> 393 pseudo_binary_subj_393   B    FALSE #> 394 pseudo_binary_subj_394   B    FALSE #> 395 pseudo_binary_subj_395   B    FALSE #> 396 pseudo_binary_subj_396   B    FALSE #> 397 pseudo_binary_subj_397   B    FALSE #> 398 pseudo_binary_subj_398   B    FALSE #> 399 pseudo_binary_subj_399   B    FALSE #> 400 pseudo_binary_subj_400   B    FALSE"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/get_time_as.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert Time Values Using Scaling Factors — get_time_as","title":"Convert Time Values Using Scaling Factors — get_time_as","text":"Convert Time Values Using Scaling Factors","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/get_time_as.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert Time Values Using Scaling Factors — get_time_as","text":"","code":"get_time_as(times, as = c(\"days\", \"weeks\", \"months\", \"years\"))"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/get_time_as.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert Time Values Using Scaling Factors — get_time_as","text":"times Numeric time values time scale convert ","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/get_time_as.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert Time Values Using Scaling Factors — get_time_as","text":"Returns numeric vector calculated times / get_time_conversion(factor = )","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/kmplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","title":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","text":"wrapper function basic_kmplot. argument setting similar maic_anchored maic_unanchored, used two functions.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/kmplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","text":"","code":"kmplot(   weights_object,   tte_ipd,   tte_pseudo_ipd,   trt_ipd,   trt_agd,   trt_common = NULL,   trt_var_ipd = \"ARM\",   trt_var_agd = \"ARM\",   km_conf_type = \"log-log\",   km_layout = c(\"all\", \"by_trial\", \"by_arm\"),   ... )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/kmplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","text":"weights_object object returned estimate_weight tte_ipd data frame individual patient data (IPD) internal trial, contain least \"USUBJID\", \"EVENT\", \"TIME\" columns column indicating treatment assignment tte_pseudo_ipd data frame pseudo IPD digitized KM curves external trial (time--event endpoint), contain least \"EVENT\", \"TIME\" trt_ipd string, name interested investigation arm internal trial dat_igd (real IPD) trt_agd string, name interested investigation arm external trial dat_pseudo (pseudo IPD) trt_common string, name common comparator internal external trial, default NULL, indicating unanchored case trt_var_ipd string, column name tte_ipd contains treatment assignment trt_var_agd string, column name tte_pseudo_ipd contains treatment assignment km_conf_type string, pass conf.type survfit km_layout string, applicable unanchored case (trt_common = NULL), indicated desired layout output KM curve. ... arguments basic_kmplot","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/kmplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","text":"unanchored case, KM plot risk set table. anchored case, depending km_layout, \"by_trial\", 2 1 plot, first KM curves (incl. weighted) IPD trial, KM curves AgD trial, risk set table. \"by_arm\", 2 1 plot, first KM curves trt_agd  trt_ipd (without weights), KM curves trt_common AgD trial IPD trial (without weights). Risk set table appended. \"\", 2 2 plot, plots \"by_trial\" \"by_arm\" without risk set table appended.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/kmplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","text":"","code":"# anchored example using kmplot  ### IPD # Read in relevant ADaM data and rename variables of interest adsl_twt #>      USUBJID ARM AGE    SEX SMOKE ECOG0 N_PR_THER SEX_MALE #> 1        xx1   A  45   Male     0     0         4        1 #> 2        xx2   A  71   Male     0     0         3        1 #> 3        xx3   A  58   Male     1     1         2        1 #> 4        xx4   A  48 Female     0     1         4        0 #> 5        xx5   A  69   Male     0     1         4        1 #> 6        xx6   A  48 Female     0     1         4        0 #> 7        xx7   A  47   Male     1     0         3        1 #> 8        xx8   A  61   Male     1     0         1        1 #> 9        xx9   A  54 Female     1     1         1        0 #> 10      xx10   A  56 Female     1     0         3        0 #> 11      xx11   A  63 Female     0     0         4        0 #> 12      xx12   A  50 Female     0     0         1        0 #> 13      xx13   A  57   Male     0     1         3        1 #> 14      xx14   A  62 Female     1     1         1        0 #> 15      xx15   A  57 Female     0     1         3        0 #> 16      xx16   A  66   Male     0     0         2        1 #> 17      xx17   A  75   Male     1     1         3        1 #> 18      xx18   A  47 Female     0     0         4        0 #> 19      xx19   A  57   Male     0     0         3        1 #> 20      xx20   A  54   Male     0     0         3        1 #> 21      xx21   A  55   Male     1     0         3        1 #> 22      xx22   A  64   Male     0     1         3        1 #> 23      xx23   A  53 Female     1     0         3        0 #> 24      xx24   A  58   Male     1     1         2        1 #> 25      xx25   A  47   Male     0     0         1        1 #> 26      xx26   A  60 Female     1     0         1        0 #> 27      xx27   A  49 Female     0     1         3        0 #> 28      xx28   A  55 Female     0     0         1        0 #> 29      xx29   A  66 Female     0     1         2        0 #> 30      xx30   A  58   Male     0     1         4        1 #> 31      xx31   A  49   Male     0     1         4        1 #> 32      xx32   A  61   Male     0     0         4        1 #> 33      xx33   A  66   Male     1     0         3        1 #> 34      xx34   A  45   Male     0     0         1        1 #> 35      xx35   A  59 Female     1     1         2        0 #> 36      xx36   A  74 Female     1     0         4        0 #> 37      xx37   A  73 Female     0     0         3        0 #> 38      xx38   A  74   Male     0     1         4        1 #> 39      xx39   A  54   Male     0     0         1        1 #> 40      xx40   A  58 Female     1     1         1        0 #> 41      xx41   A  61 Female     0     1         3        0 #> 42      xx42   A  47 Female     1     1         2        0 #> 43      xx43   A  73 Female     1     1         2        0 #> 44      xx44   A  68   Male     0     0         1        1 #> 45      xx45   A  49 Female     0     0         3        0 #> 46      xx46   A  71 Female     0     0         2        0 #> 47      xx47   A  70   Male     0     1         4        1 #> 48      xx48   A  62 Female     1     0         1        0 #> 49      xx49   A  49   Male     0     0         1        1 #> 50      xx50   A  74 Female     0     0         1        0 #> 51      xx51   A  46 Female     0     1         3        0 #> 52      xx52   A  68 Female     1     0         3        0 #> 53      xx53   A  46   Male     1     0         2        1 #> 54      xx54   A  75 Female     1     1         3        0 #> 55      xx55   A  47 Female     0     0         3        0 #> 56      xx56   A  56   Male     0     1         3        1 #> 57      xx57   A  72 Female     0     0         3        0 #> 58      xx58   A  57   Male     1     1         4        1 #> 59      xx59   A  46   Male     0     0         1        1 #> 60      xx60   A  56 Female     1     1         1        0 #> 61      xx61   A  73   Male     0     1         2        1 #> 62      xx62   A  60 Female     1     1         3        0 #> 63      xx63   A  75   Male     0     0         2        1 #> 64      xx64   A  69 Female     1     1         2        0 #> 65      xx65   A  47 Female     0     1         1        0 #> 66      xx66   A  74   Male     0     0         4        1 #> 67      xx67   A  71 Female     0     1         1        0 #> 68      xx68   A  49 Female     1     1         1        0 #> 69      xx69   A  68   Male     0     0         3        1 #> 70      xx70   A  49   Male     0     1         1        1 #> 71      xx71   A  70   Male     0     1         1        1 #> 72      xx72   A  45 Female     0     0         2        0 #> 73      xx73   A  47 Female     0     1         3        0 #> 74      xx74   A  58   Male     0     1         3        1 #> 75      xx75   A  49 Female     0     1         4        0 #> 76      xx76   A  68 Female     0     0         1        0 #> 77      xx77   A  60   Male     0     0         4        1 #> 78      xx78   A  45 Female     1     0         1        0 #> 79      xx79   A  57 Female     0     0         1        0 #> 80      xx80   A  50 Female     0     1         1        0 #> 81      xx81   A  63   Male     0     1         3        1 #> 82      xx82   A  47 Female     0     0         2        0 #> 83      xx83   A  68 Female     0     1         4        0 #> 84      xx84   A  51   Male     0     0         4        1 #> 85      xx85   A  60   Male     0     0         1        1 #> 86      xx86   A  52 Female     1     0         4        0 #> 87      xx87   A  69   Male     1     1         1        1 #> 88      xx88   A  70 Female     0     1         4        0 #> 89      xx89   A  72   Male     0     0         2        1 #> 90      xx90   A  46 Female     0     1         1        0 #> 91      xx91   A  51   Male     1     0         4        1 #> 92      xx92   A  69 Female     0     1         1        0 #> 93      xx93   A  66   Male     0     0         1        1 #> 94      xx94   A  73   Male     0     1         1        1 #> 95      xx95   A  73 Female     1     0         3        0 #> 96      xx96   A  62 Female     0     1         2        0 #> 97      xx97   A  55 Female     0     0         4        0 #> 98      xx98   A  67   Male     1     0         3        1 #> 99      xx99   A  54 Female     1     0         3        0 #> 100    xx100   A  52 Female     1     1         4        0 #> 101    xx101   A  57   Male     0     0         2        1 #> 102    xx102   A  57 Female     1     1         3        0 #> 103    xx103   A  57   Male     0     0         3        1 #> 104    xx104   A  67 Female     1     1         2        0 #> 105    xx105   A  67 Female     1     1         2        0 #> 106    xx106   A  74 Female     1     1         2        0 #> 107    xx107   A  72 Female     1     0         2        0 #> 108    xx108   A  73 Female     0     0         3        0 #> 109    xx109   A  57 Female     0     0         4        0 #> 110    xx110   A  69 Female     1     0         1        0 #> 111    xx111   A  55   Male     0     0         1        1 #> 112    xx112   A  74 Female     0     0         4        0 #> 113    xx113   A  68 Female     0     0         4        0 #> 114    xx114   A  53   Male     0     0         2        1 #> 115    xx115   A  69   Male     0     0         2        1 #> 116    xx116   A  68   Male     0     1         2        1 #> 117    xx117   A  58   Male     0     0         1        1 #> 118    xx118   A  64 Female     0     0         3        0 #> 119    xx119   A  71   Male     0     0         1        1 #> 120    xx120   A  69 Female     0     1         2        0 #> 121    xx121   A  64 Female     1     0         4        0 #> 122    xx122   A  64   Male     1     0         1        1 #> 123    xx123   A  55   Male     0     1         3        1 #> 124    xx124   A  74   Male     0     0         3        1 #> 125    xx125   A  50   Male     0     1         3        1 #> 126    xx126   A  68   Male     0     1         1        1 #> 127    xx127   A  60   Male     0     0         2        1 #> 128    xx128   A  59 Female     0     0         3        0 #> 129    xx129   A  71 Female     1     0         3        0 #> 130    xx130   A  69   Male     0     1         4        1 #> 131    xx131   A  56 Female     0     1         1        0 #> 132    xx132   A  51   Male     0     1         3        1 #> 133    xx133   A  65   Male     0     1         2        1 #> 134    xx134   A  45   Male     1     1         3        1 #> 135    xx135   A  49 Female     0     0         2        0 #> 136    xx136   A  74 Female     1     0         4        0 #> 137    xx137   A  71 Female     1     1         3        0 #> 138    xx138   A  65   Male     0     0         1        1 #> 139    xx139   A  67 Female     0     0         2        0 #> 140    xx140   A  48 Female     0     0         3        0 #> 141    xx141   A  70 Female     0     1         4        0 #> 142    xx142   A  72 Female     0     0         1        0 #> 143    xx143   A  53   Male     1     1         4        1 #> 144    xx144   A  68 Female     1     0         3        0 #> 145    xx145   A  65   Male     0     1         1        1 #> 146    xx146   A  70 Female     1     1         4        0 #> 147    xx147   A  58   Male     0     0         2        1 #> 148    xx148   A  68 Female     0     1         4        0 #> 149    xx149   A  54 Female     0     0         4        0 #> 150    xx150   A  75 Female     1     1         4        0 #> 151    xx151   A  49 Female     1     1         3        0 #> 152    xx152   A  60   Male     0     0         4        1 #> 153    xx153   A  45 Female     1     0         4        0 #> 154    xx154   A  73 Female     1     0         3        0 #> 155    xx155   A  71 Female     0     1         3        0 #> 156    xx156   A  73 Female     0     0         3        0 #> 157    xx157   A  58 Female     0     0         4        0 #> 158    xx158   A  59 Female     0     1         2        0 #> 159    xx159   A  75 Female     0     1         2        0 #> 160    xx160   A  65   Male     0     0         2        1 #> 161    xx161   A  48   Male     1     0         2        1 #> 162    xx162   A  63 Female     0     1         2        0 #> 163    xx163   A  74 Female     0     0         3        0 #> 164    xx164   A  64 Female     0     0         3        0 #> 165    xx165   A  49 Female     0     0         1        0 #> 166    xx166   A  56 Female     0     1         4        0 #> 167    xx167   A  56 Female     0     1         2        0 #> 168    xx168   A  48 Female     0     0         1        0 #> 169    xx169   A  65   Male     0     1         3        1 #> 170    xx170   A  53 Female     0     0         3        0 #> 171    xx171   A  72   Male     0     0         2        1 #> 172    xx172   A  75 Female     1     0         4        0 #> 173    xx173   A  70 Female     0     1         2        0 #> 174    xx174   A  53 Female     1     0         2        0 #> 175    xx175   A  45 Female     1     1         3        0 #> 176    xx176   A  53 Female     1     1         3        0 #> 177    xx177   A  52 Female     0     1         2        0 #> 178    xx178   A  61 Female     1     0         4        0 #> 179    xx179   A  70   Male     0     0         3        1 #> 180    xx180   A  58 Female     0     0         3        0 #> 181    xx181   A  54   Male     0     0         1        1 #> 182    xx182   A  53   Male     0     0         2        1 #> 183    xx183   A  74   Male     0     0         2        1 #> 184    xx184   A  64   Male     0     1         3        1 #> 185    xx185   A  52   Male     0     0         1        1 #> 186    xx186   A  73 Female     0     0         3        0 #> 187    xx187   A  55 Female     1     0         4        0 #> 188    xx188   A  71 Female     0     0         3        0 #> 189    xx189   A  57 Female     1     0         3        0 #> 190    xx190   A  49 Female     1     0         2        0 #> 191    xx191   A  69   Male     0     0         4        1 #> 192    xx192   A  74 Female     1     0         2        0 #> 193    xx193   A  59 Female     0     0         3        0 #> 194    xx194   A  53   Male     0     0         4        1 #> 195    xx195   A  52 Female     0     1         3        0 #> 196    xx196   A  47 Female     1     1         1        0 #> 197    xx197   A  61 Female     0     0         4        0 #> 198    xx198   A  51 Female     0     0         2        0 #> 199    xx199   A  62 Female     1     0         1        0 #> 200    xx200   A  59 Female     1     0         2        0 #> 201    xx201   A  58   Male     0     0         2       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218    xx218   A  60 Female     1     0         1        0 #> 219    xx219   A  71 Female     0     0         3        0 #> 220    xx220   A  54 Female     0     0         1        0 #> 221    xx221   A  51   Male     1     0         1        1 #> 222    xx222   A  71   Male     0     1         3        1 #> 223    xx223   A  47   Male     0     0         2        1 #> 224    xx224   A  65   Male     0     0         1        1 #> 225    xx225   A  53 Female     0     0         1        0 #> 226    xx226   A  56 Female     0     0         1        0 #> 227    xx227   A  51   Male     1     0         4        1 #> 228    xx228   A  68 Female     0     0         2        0 #> 229    xx229   A  75 Female     0     1         1        0 #> 230    xx230   A  49 Female     0     0         2        0 #> 231    xx231   A  74   Male     1     0         1        1 #> 232    xx232   A  66 Female     0     0         1        0 #> 233    xx233   A  74 Female     0     0         1        0 #> 234    xx234   A  62 Female     0     1         2        0 #> 235    xx235   A  53 Female     0     0         1        0 #> 236    xx236   A  62 Female     0     1         1        0 #> 237    xx237   A  70 Female     1     0         3        0 #> 238    xx238   A  60 Female     1     0         2        0 #> 239    xx239   A  72   Male     0     0         1        1 #> 240    xx240   A  74 Female     1     0         3        0 #> 241    xx241   A  47 Female     1     0         3        0 #> 242    xx242   A  54 Female     0     1         3        0 #> 243    xx243   A  65 Female     0     1         2        0 #> 244    xx244   A  74   Male     0     1         1        1 #> 245    xx245   A  61   Male     0     1         2        1 #> 246    xx246   A  54 Female     1     0         3        0 #> 247    xx247   A  65   Male     0     0         3        1 #> 248    xx248   A  72 Female     1     0         3        0 #> 249    xx249   A  71 Female     1     1         2        0 #> 250    xx250   A  65   Male     0     0         3        1 #> 251    xx251   A  58   Male     0     1         1        1 #> 252    xx252   A  46   Male     0     0         1        1 #> 253    xx253   A  53 Female     1     0         4        0 #> 254    xx254   A  71 Female     1     0         4        0 #> 255    xx255   A  47   Male     0     1         3        1 #> 256    xx256   A  45   Male     0     0         1        1 #> 257    xx257   A  50   Male     0     0         3        1 #> 258    xx258   A  67 Female     1     0         3        0 #> 259    xx259   A  72   Male     1     0         2        1 #> 260    xx260   A  45 Female     0     1         2        0 #> 261    xx261   A  75 Female     1     0         1        0 #> 262    xx262   A  65   Male     0     0         3        1 #> 263    xx263   A  60 Female     0     1         3        0 #> 264    xx264   A  75 Female     0     1         4        0 #> 265    xx265   A  60 Female     1     0         4        0 #> 266    xx266   A  49 Female     1     0         2        0 #> 267    xx267   A  58 Female     0     1         1        0 #> 268    xx268   A  57   Male     1     1         3        1 #> 269    xx269   A  69   Male     1     0         2        1 #> 270    xx270   A  51   Male     0     1         1        1 #> 271    xx271   A  54 Female     1     0         4        0 #> 272    xx272   A  55   Male     0     1         3        1 #> 273    xx273   A  49 Female     0     0         4        0 #> 274    xx274   A  74 Female     1     0         1        0 #> 275    xx275   A  55   Male     1     0         1        1 #> 276    xx276   A  52 Female     1     0         1        0 #> 277    xx277   A  65   Male     0     0         2        1 #> 278    xx278   A  70 Female     1     0         1        0 #> 279    xx279   A  66 Female     1     1         2        0 #> 280    xx280   A  63 Female     0     1         4        0 #> 281    xx281   A  61 Female     0     1         3        0 #> 282    xx282   A  65   Male     0     1         2        1 #> 283    xx283   A  73   Male     0     0         2        1 #> 284    xx284   A  55 Female     1     1         4        0 #> 285    xx285   A  56 Female     1     1         4        0 #> 286    xx286   A  68 Female     0     1         1        0 #> 287    xx287   A  74 Female     1     0         4        0 #> 288    xx288   A  67 Female     0     0         2        0 #> 289    xx289   A  66   Male     0     1         3        1 #> 290    xx290   A  48 Female     0     0         3        0 #> 291    xx291   A  49 Female     1     1         3        0 #> 292    xx292   A  60 Female     1     1         1        0 #> 293    xx293   A  69 Female     0     1         4        0 #> 294    xx294   A  58 Female     0     1         3        0 #> 295    xx295   A  45 Female     1     1         4        0 #> 296    xx296   A  49 Female     0     1         1        0 #> 297    xx297   A  67 Female     1     0         4        0 #> 298    xx298   A  63   Male     0     0         4        1 #> 299    xx299   A  50 Female     0     0         2        0 #> 300    xx300   A  68 Female     0     1         3        0 #> 301    xx301   A  53   Male     0     1         2        1 #> 302    xx302   A  63   Male     0     1         2        1 #> 303    xx303   A  58   Male     0     0         4        1 #> 304    xx304   A  70 Female     0     1         4        0 #> 305    xx305   A  56 Female     0     0         1        0 #> 306    xx306   A  56   Male     0     1         3        1 #> 307    xx307   A  61 Female     0     0         3        0 #> 308    xx308   A  72   Male     0     0         2        1 #> 309    xx309   A  51   Male     0     1         1        1 #> 310    xx310   A  72   Male     0     0         4        1 #> 311    xx311   A  64 Female     0     0         3        0 #> 312    xx312   A  59   Male     0     0         2        1 #> 313    xx313   A  75 Female     0     0         3        0 #> 314    xx314   A  75 Female     0     0         1        0 #> 315    xx315   A  74   Male     0     0         2        1 #> 316    xx316   A  54   Male     0     1         4        1 #> 317    xx317   A  55 Female     0     0         3        0 #> 318    xx318   A  52 Female     1     0         1        0 #> 319    xx319   A  46 Female     0     1         1        0 #> 320    xx320   A  53   Male     0     0         1        1 #> 321    xx321   A  54 Female     0     1         1        0 #> 322    xx322   A  62 Female     0     0         2        0 #> 323    xx323   A  54   Male     1     0         4        1 #> 324    xx324   A  56 Female     0     0         4        0 #> 325    xx325   A  48 Female     0     0         1        0 #> 326    xx326   A  52 Female     0     1         1        0 #> 327    xx327   A  55 Female     0     1         3        0 #> 328    xx328   A  69 Female     0     0         2        0 #> 329    xx329   A  48 Female     0     0         1        0 #> 330    xx330   A  48 Female     1     1         3        0 #> 331    xx331   A  60   Male     0     0         3        1 #> 332    xx332   A  74 Female     0     1         2        0 #> 333    xx333   A  45 Female     0     0         1        0 #> 334    xx334   A  64   Male     1     0         1        1 #> 335    xx335   A  75 Female     1     1         3        0 #> 336    xx336   A  62 Female     0     0         3        0 #> 337    xx337   A  71   Male     0     0         4        1 #> 338    xx338   A  48 Female     1     1         3        0 #> 339    xx339   A  53 Female     0     0         2        0 #> 340    xx340   A  62   Male     0     0         3        1 #> 341    xx341   A  69 Female     0     0         2        0 #> 342    xx342   A  72 Female     0     0         1        0 #> 343    xx343   A  61 Female     0     0         2        0 #> 344    xx344   A  47   Male     0     0         2        1 #> 345    xx345   A  58   Male     0     1         1        1 #> 346    xx346   A  52 Female     0     0         4        0 #> 347    xx347   A  49 Female     0     0         4        0 #> 348    xx348   A  51 Female     0     0         1        0 #> 349    xx349   A  51 Female     1     1         2        0 #> 350    xx350   A  72   Male     0     0         3        1 #> 351    xx351   A  68   Male     0     0         3        1 #> 352    xx352   A  49 Female     0     0         4        0 #> 353    xx353   A  45 Female     0     0         3        0 #> 354    xx354   A  49 Female     0     0         3        0 #> 355    xx355   A  65   Male     0     1         2        1 #> 356    xx356   A  56   Male     0     0         2        1 #> 357    xx357   A  45 Female     1     1         1        0 #> 358    xx358   A  57   Male     1     0         2        1 #> 359    xx359   A  53   Male     1     1         2        1 #> 360    xx360   A  65 Female     0     0         2        0 #> 361    xx361   A  57   Male     0     1         4        1 #> 362    xx362   A  55 Female     0     1         4        0 #> 363    xx363   A  57   Male     0     1         2        1 #> 364    xx364   A  46 Female     0     1         4        0 #> 365    xx365   A  69 Female     0     1         1        0 #> 366    xx366   A  67 Female     0     1         3        0 #> 367    xx367   A  55   Male     0     0         1        1 #> 368    xx368   A  53 Female     0     1         3        0 #> 369    xx369   A  46 Female     0     0         3        0 #> 370    xx370   A  71   Male     0     0         4        1 #> 371    xx371   A  68   Male     0     1         1        1 #> 372    xx372   A  49 Female     0     1         3        0 #> 373    xx373   A  51 Female     0     0         3        0 #> 374    xx374   A  65 Female     1     1         3        0 #> 375    xx375   A  55 Female     0     0         4        0 #> 376    xx376   A  53   Male     0     0         4        1 #> 377    xx377   A  65 Female     0     1         4        0 #> 378    xx378   A  72 Female     1     1         4        0 #> 379    xx379   A  61   Male     0     1         1        1 #> 380    xx380   A  73 Female     0     0         1        0 #> 381    xx381   A  62 Female     1     1         2        0 #> 382    xx382   A  46   Male     0     0         2        1 #> 383    xx383   A  51   Male     0     1         4        1 #> 384    xx384   A  60   Male     1     1         4        1 #> 385    xx385   A  56 Female     0     1         3        0 #> 386    xx386   A  69 Female     0     1         1        0 #> 387    xx387   A  58 Female     1     1         2        0 #> 388    xx388   A  58 Female     1     1         3        0 #> 389    xx389   A  53 Female     0     1         2        0 #> 390    xx390   A  47 Female     0     0         2        0 #> 391    xx391   A  59   Male     0     1         2        1 #> 392    xx392   A  47 Female     0     0         4        0 #> 393    xx393   A  60 Female     1     0         4        0 #> 394    xx394   A  73 Female     0     0         4        0 #> 395    xx395   A  60   Male     0     0         1        1 #> 396    xx396   A  75   Male     0     0         4        1 #> 397    xx397   A  65 Female     0     0         3        0 #> 398    xx398   A  68   Male     0     0         1        1 #> 399    xx399   A  55 Female     0     0         4        0 #> 400    xx400   A  46 Female     1     0         4        0 #> 401    xx401   A  45 Female     0     1         1        0 #> 402    xx402   A  70   Male     1     1         3        1 #> 403    xx403   A  56 Female     0     1         4        0 #> 404    xx404   A  62 Female     0     0         3        0 #> 405    xx405   A  49   Male     0     0         4        1 #> 406    xx406   A  52 Female     0     0         4        0 #> 407    xx407   A  67 Female     0     1         4        0 #> 408    xx408   A  50 Female     1     1         1        0 #> 409    xx409   A  68 Female     1     1         2        0 #> 410    xx410   A  54 Female     0     0         4        0 #> 411    xx411   A  65   Male     0     0         4        1 #> 412    xx412   A  55 Female     0     1         2        0 #> 413    xx413   A  53 Female     1     1         4        0 #> 414    xx414   A  71 Female     0     0         2        0 #> 415    xx415   A  48   Male     0     0         1        1 #> 416    xx416   A  54 Female     0     1         3        0 #> 417    xx417   A  75   Male     1     0         3        1 #> 418    xx418   A  53 Female     0     0         2        0 #> 419    xx419   A  50 Female     1     0         4        0 #> 420    xx420   A  64 Female     1     0         1        0 #> 421    xx421   A  65 Female     0     0         2        0 #> 422    xx422   A  65 Female     0     1         4        0 #> 423    xx423   A  60 Female     1     0         2        0 #> 424    xx424   A  70 Female     1     0         3        0 #> 425    xx425   A  51 Female     0     0         2        0 #> 426    xx426   A  45 Female     0     1         1        0 #> 427    xx427   A  75 Female     1     0         2        0 #> 428    xx428   A  52 Female     1     0         1        0 #> 429    xx429   A  70   Male     0     0         4        1 #> 430    xx430   A  69 Female     1     1         3        0 #> 431    xx431   A  64 Female     0     0         2        0 #> 432    xx432   A  68 Female     1     0         1        0 #> 433    xx433   A  51   Male     1     0         1        1 #> 434    xx434   A  59 Female     0     1         2        0 #> 435    xx435   A  57 Female     0     0         1        0 #> 436    xx436   A  47   Male     0     0         2        1 #> 437    xx437   A  65   Male     0     1         1        1 #> 438    xx438   A  65   Male     0     1         1        1 #> 439    xx439   A  65   Male     0     0         2        1 #> 440    xx440   A  46   Male     0     0         2        1 #> 441    xx441   A  64 Female     0     0         3        0 #> 442    xx442   A  57 Female     0     1         4        0 #> 443    xx443   A  67 Female     0     1         3        0 #> 444    xx444   A  61 Female     1     0         3        0 #> 445    xx445   A  56   Male     0     0         4        1 #> 446    xx446   A  52   Male     0     0         3        1 #> 447    xx447   A  74 Female     1     1         3        0 #> 448    xx448   A  75   Male     0     1         3        1 #> 449    xx449   A  58   Male     0     1         3        1 #> 450    xx450   A  57 Female     0     1         4        0 #> 451    xx451   A  55 Female     0     0         1        0 #> 452    xx452   A  53 Female     1     0         1        0 #> 453    xx453   A  75   Male     1     0         4        1 #> 454    xx454   A  65 Female     0     1         3        0 #> 455    xx455   A  65 Female     0     0         4        0 #> 456    xx456   A  58   Male     0     0         4        1 #> 457    xx457   A  71 Female     0     0         3        0 #> 458    xx458   A  71   Male     0     0         4        1 #> 459    xx459   A  59   Male     1     0         4        1 #> 460    xx460   A  46   Male     1     0         4        1 #> 461    xx461   A  51 Female     0     0         4        0 #> 462    xx462   A  56 Female     0     1         4        0 #> 463    xx463   A  66 Female     0     0         1        0 #> 464    xx464   A  59 Female     1     0         2        0 #> 465    xx465   A  48 Female     1     0         2        0 #> 466    xx466   A  68 Female     0     0         4        0 #> 467    xx467   A  57 Female     0     0         4        0 #> 468    xx468   A  63   Male     0     0         4        1 #> 469    xx469   A  62   Male     0     1         3        1 #> 470    xx470   A  70 Female     0     0         3        0 #> 471    xx471   A  55 Female     0     1         2        0 #> 472    xx472   A  56   Male     1     0         3        1 #> 473    xx473   A  51   Male     1     0         1        1 #> 474    xx474   A  51 Female     0     0         4        0 #> 475    xx475   A  46 Female     1     1         3        0 #> 476   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A  67   Male     1     1         4        1 #> 493    xx493   A  45   Male     1     1         2        1 #> 494    xx494   A  71 Female     0     1         2        0 #> 495    xx495   A  55   Male     0     1         3        1 #> 496    xx496   A  72 Female     0     0         4        0 #> 497    xx497   A  48 Female     1     0         3        0 #> 498    xx498   A  68 Female     1     0         1        0 #> 499    xx499   A  45 Female     0     0         1        0 #> 500    xx500   A  58 Female     1     1         3        0 #> 1721   yyxx1   C  75 Female     1     0         4        0 #> 3651  yyxx10   C  69 Female     0     1         1        0 #> 4881 yyxx100   C  51   Male     1     1         1        1 #> 2281 yyxx101   C  68 Female     0     0         2        0 #> 4231 yyxx102   C  60 Female     1     0         2        0 #> 1981 yyxx103   C  51 Female     0     0         2        0 #> 2401 yyxx104   C  74 Female     1     0         3        0 #> 910  yyxx105   C  54 Female     1     1         1        0 #> 4410 yyxx106   C  68   Male     0     0         1        1 #> 2221 yyxx107   C  71   Male     0     1         3        1 #> 741  yyxx108   C  58   Male     0     1         3        1 #> 710  yyxx109   C  47   Male     1     0         3        1 #> 1310  yyxx11   C  57   Male     0     1         3        1 #> 3571 yyxx110   C  45 Female     1     1         1        0 #> 2551 yyxx111   C  47   Male     0     1         3        1 #> 3831 yyxx112   C  51   Male     0     1         4        1 #> 1121 yyxx113   C  74 Female     0     0         4        0 #> 4841 yyxx114   C  45 Female     0     0         2        0 #> 731  yyxx115   C  47 Female     0     1         3        0 #> 4771 yyxx116   C  71   Male     0     1         4        1 #> 4371 yyxx117   C  65   Male     0     1         1        1 #> 971  yyxx118   C  55 Female     0     0         4        0 #> 1451 yyxx119   C  65   Male     0     1         1        1 #> 2701  yyxx12   C  51   Male   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Male     0     1         3        1 #> 2781 yyxx222   C  70 Female     1     0         1        0 #> 4651 yyxx223   C  48 Female     1     0         2        0 #> 1151 yyxx224   C  69   Male     0     0         2        1 #> 1361 yyxx225   C  74 Female     1     0         4        0 #> 1110 yyxx226   C  63 Female     0     0         4        0 #> 4441 yyxx227   C  61 Female     1     0         3        0 #> 2541 yyxx228   C  71 Female     1     0         4        0 #> 4271 yyxx229   C  75 Female     1     0         2        0 #> 2311  yyxx23   C  74   Male     1     0         1        1 #> 3971 yyxx230   C  65 Female     0     0         3        0 #> 3621 yyxx231   C  55 Female     0     1         4        0 #> 3521 yyxx232   C  49 Female     0     0         4        0 #> 3851 yyxx233   C  56 Female     0     1         3        0 #> 4291 yyxx234   C  70   Male     0     0         4        1 #> 881  yyxx235   C  70 Female     0     1         4        0 #> 3321 yyxx236   C  74 Female     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1610  yyxx62   C  66   Male     0     0         2        1 #> 4091  yyxx63   C  68 Female     1     1         2        0 #> 1491  yyxx64   C  54 Female     0     0         4        0 #> 1951  yyxx65   C  52 Female     0     1         3        0 #> 1100  yyxx66   C  45   Male     0     0         4        1 #> 3611  yyxx67   C  57   Male     0     1         4        1 #> 1091  yyxx68   C  57 Female     0     0         4        0 #> 2801  yyxx69   C  63 Female     0     1         4        0 #> 3301   yyxx7   C  48 Female     1     1         3        0 #> 2921  yyxx70   C  60 Female     1     1         1        0 #> 2441  yyxx71   C  74   Male     0     1         1        1 #> 3231  yyxx72   C  54   Male     1     0         4        1 #> 941   yyxx73   C  73   Male     0     1         1        1 #> 1571  yyxx74   C  58 Female     0     0         4        0 #> 4901  yyxx75   C  60 Female     0     1         3        0 #> 4061  yyxx76   C  52 Female     0     0         4        0 #> 1941  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Overall Survival    1 101.66319265 #> 6        xx6   A 237.0593233  DAYS      OS Overall Survival    0 237.05932332 #> 7        xx7   A 337.3663345  DAYS      OS Overall Survival    1 337.36633453 #> 8        xx8   A 180.6996302  DAYS      OS Overall Survival    0 180.69963022 #> 9        xx9   A 156.3530945  DAYS      OS Overall Survival    1 156.35309454 #> 10      xx10   A 126.0500878  DAYS      OS Overall Survival    1 126.05008778 #> 11      xx11   A   2.9969434  DAYS      OS Overall Survival    1   2.99694342 #> 12      xx12   A 189.3613424  DAYS      OS Overall Survival    1 189.36134243 #> 13      xx13   A 137.1939184  DAYS      OS Overall Survival    0 137.19391844 #> 14      xx14   A   0.4303082  DAYS      OS Overall Survival    0   0.43030823 #> 15      xx15   A 238.4480442  DAYS      OS Overall Survival    1 238.44804418 #> 16      xx16   A  64.6872731  DAYS      OS Overall Survival    1  64.68727307 #> 17      xx17   A  62.6283981  DAYS      OS Overall Survival    1  62.62839808 #> 18      xx18   A  61.8689337  DAYS      OS Overall Survival    0  61.86893374 #> 19      xx19   A 172.9990508  DAYS      OS Overall Survival    1 172.99905082 #> 20      xx20   A 500.0000000  DAYS      OS Overall Survival    1 500.00000000 #> 21      xx21   A 404.9665031  DAYS      OS Overall Survival    0 404.96650312 #> 22      xx22   A   8.6078129  DAYS      OS Overall Survival    0   8.60781293 #> 23      xx23   A   2.6752029  DAYS      OS Overall Survival    0   2.67520289 #> 24      xx24   A  37.1862175  DAYS      OS Overall Survival    0  37.18621753 #> 25      xx25   A  72.3471050  DAYS      OS Overall Survival    0  72.34710499 #> 26      xx26   A 149.0394051  DAYS      OS Overall Survival    1 149.03940513 #> 27      xx27   A 271.3474961  DAYS      OS Overall Survival    1 271.34749606 #> 28      xx28   A 132.7882079  DAYS      OS Overall Survival    1 132.78820785 #> 29      xx29   A 124.8525835  DAYS      OS Overall Survival    0 124.85258351 #> 30      xx30  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Overall Survival    0   7.43789943 #> 43      xx43   A  53.9061795  DAYS      OS Overall Survival    1  53.90617948 #> 44      xx44   A  42.4329399  DAYS      OS Overall Survival    1  42.43293987 #> 45      xx45   A  22.8324762  DAYS      OS Overall Survival    0  22.83247621 #> 46      xx46   A 152.6837825  DAYS      OS Overall Survival    1 152.68378250 #> 47      xx47   A   5.1546502  DAYS      OS Overall Survival    0   5.15465015 #> 48      xx48   A  13.8523925  DAYS      OS Overall Survival    1  13.85239252 #> 49      xx49   A 317.5266710  DAYS      OS Overall Survival    1 317.52667101 #> 50      xx50   A 222.4007682  DAYS      OS Overall Survival    1 222.40076824 #> 51      xx51   A   7.2337488  DAYS      OS Overall Survival    0   7.23374881 #> 52      xx52   A  60.2611923  DAYS      OS Overall Survival    1  60.26119228 #> 53      xx53   A 176.9610656  DAYS      OS Overall Survival    0 176.96106564 #> 54      xx54   A  12.6413833  DAYS      OS Overall Survival    0  12.64138325 #> 55      xx55   A  63.5048707  DAYS      OS Overall Survival    1  63.50487075 #> 56      xx56   A 329.3894821  DAYS      OS Overall Survival    1 329.38948205 #> 57      xx57   A  54.6861744  DAYS      OS Overall Survival    1  54.68617443 #> 58      xx58   A 187.3284384  DAYS      OS Overall Survival    1 187.32843844 #> 59      xx59   A 108.2199961  DAYS      OS Overall Survival    1 108.21999606 #> 60      xx60   A  16.0028888  DAYS      OS Overall Survival    1  16.00288877 #> 61      xx61   A  59.6044136  DAYS      OS Overall Survival    1  59.60441362 #> 62      xx62   A 176.6590842  DAYS      OS Overall Survival    0 176.65908421 #> 63      xx63   A  49.6663522  DAYS      OS Overall Survival    1  49.66635216 #> 64      xx64   A  87.2997415  DAYS      OS Overall Survival    0  87.29974150 #> 65      xx65   A 326.3333469  DAYS      OS Overall Survival    0 326.33334691 #> 66      xx66   A 173.9223130  DAYS      OS Overall Survival    0 173.92231295 #> 67      xx67  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Overall Survival    1  67.68559019 #> 80      xx80   A 187.2930109  DAYS      OS Overall Survival    0 187.29301095 #> 81      xx81   A 205.1596244  DAYS      OS Overall Survival    1 205.15962436 #> 82      xx82   A 133.9676829  DAYS      OS Overall Survival    1 133.96768294 #> 83      xx83   A  99.3645367  DAYS      OS Overall Survival    1  99.36453671 #> 84      xx84   A 152.3036359  DAYS      OS Overall Survival    0 152.30363591 #> 85      xx85   A 158.5194710  DAYS      OS Overall Survival    0 158.51947100 #> 86      xx86   A 203.4020672  DAYS      OS Overall Survival    0 203.40206722 #> 87      xx87   A 336.8420868  DAYS      OS Overall Survival    0 336.84208675 #> 88      xx88   A  25.6490953  DAYS      OS Overall Survival    1  25.64909528 #> 89      xx89   A 352.4383084  DAYS      OS Overall Survival    1 352.43830838 #> 90      xx90   A  33.8799882  DAYS      OS Overall Survival    1  33.87998822 #> 91      xx91   A  15.8860250  DAYS      OS Overall Survival    1  15.88602505 #> 92      xx92   A 245.3458498  DAYS      OS Overall Survival    0 245.34584981 #> 93      xx93   A 425.2838139  DAYS      OS Overall Survival    1 425.28381391 #> 94      xx94   A  28.5374060  DAYS      OS Overall Survival    1  28.53740598 #> 95      xx95   A  94.0876845  DAYS      OS Overall Survival    0  94.08768445 #> 96      xx96   A 109.4524597  DAYS      OS Overall Survival    1 109.45245972 #> 97      xx97   A 313.3722027  DAYS      OS Overall Survival    1 313.37220272 #> 98      xx98   A  50.3150662  DAYS      OS Overall Survival    0  50.31506616 #> 99      xx99   A  52.4467296  DAYS      OS Overall Survival    0  52.44672963 #> 100    xx100   A  10.2701408  DAYS      OS Overall Survival    1  10.27014082 #> 101    xx101   A   6.8633253  DAYS      OS Overall Survival    1   6.86332527 #> 102    xx102   A  11.1663130  DAYS      OS Overall Survival    0  11.16631299 #> 103    xx103   A 208.5440529  DAYS      OS Overall Survival    1 208.54405286 #> 104    xx104   A 453.8755271  DAYS      OS Overall Survival    1 453.87552714 #> 105    xx105   A  13.7869996  DAYS      OS Overall Survival    1  13.78699955 #> 106    xx106   A 350.2207221  DAYS      OS Overall Survival    0 350.22072212 #> 107    xx107   A  89.6167692  DAYS      OS Overall Survival    0  89.61676923 #> 108    xx108   A   0.2046028  DAYS      OS Overall Survival    1   0.20460281 #> 109    xx109   A  30.8717178  DAYS      OS Overall Survival    0  30.87171779 #> 110    xx110   A 280.3909651  DAYS      OS Overall Survival    1 280.39096507 #> 111    xx111   A  84.7166775  DAYS      OS Overall Survival    1  84.71667753 #> 112    xx112   A   8.3998372  DAYS      OS Overall Survival    1   8.39983716 #> 113    xx113   A 135.1019149  DAYS      OS Overall Survival    1 135.10191489 #> 114    xx114   A 126.7874323  DAYS      OS Overall Survival    1 126.78743229 #> 115    xx115   A 174.5461325  DAYS      OS Overall Survival    0 174.54613253 #> 116    xx116   A 161.5846889  DAYS      OS Overall Survival    0 161.58468886 #> 117    xx117   A  66.0390115  DAYS      OS Overall Survival    1  66.03901152 #> 118    xx118   A 104.5408470  DAYS      OS Overall Survival    1 104.54084695 #> 119    xx119   A 209.6796639  DAYS      OS Overall Survival    0 209.67966393 #> 120    xx120   A   1.2808132  DAYS      OS Overall Survival    1   1.28081324 #> 121    xx121   A  45.4720260  DAYS      OS Overall Survival    1  45.47202599 #> 122    xx122   A  43.6389358  DAYS      OS Overall Survival    1  43.63893577 #> 123    xx123   A  13.1747627  DAYS      OS Overall Survival    1  13.17476269 #> 124    xx124   A 204.8322250  DAYS      OS Overall Survival    0 204.83222501 #> 125    xx125   A  76.2621398  DAYS      OS Overall Survival    0  76.26213977 #> 126    xx126   A 179.5550296  DAYS      OS Overall Survival    1 179.55502961 #> 127    xx127   A 153.0189691  DAYS      OS Overall Survival    1 153.01896914 #> 128    xx128   A   2.0300958  DAYS      OS Overall Survival    1   2.03009579 #> 129    xx129   A 168.2281259  DAYS      OS Overall Survival    0 168.22812592 #> 130    xx130   A  55.0706664  DAYS      OS Overall Survival    1  55.07066639 #> 131    xx131   A  31.4891944  DAYS      OS Overall Survival    1  31.48919436 #> 132    xx132   A 168.4294215  DAYS      OS Overall Survival    1 168.42942147 #> 133    xx133   A 213.0554394  DAYS      OS Overall Survival    0 213.05543937 #> 134    xx134   A  58.1487885  DAYS      OS Overall Survival    0  58.14878850 #> 135    xx135   A 139.0211403  DAYS      OS Overall Survival    1 139.02114031 #> 136    xx136   A 104.1193509  DAYS      OS Overall Survival    1 104.11935093 #> 137    xx137   A  46.1566080  DAYS      OS Overall Survival    1  46.15660801 #> 138    xx138   A 158.4800477  DAYS      OS Overall Survival    1 158.48004773 #> 139    xx139   A 295.1617767  DAYS      OS Overall Survival    0 295.16177671 #> 140    xx140   A  42.5864914  DAYS      OS Overall Survival    1  42.58649144 #> 141    xx141   A 127.9743928  DAYS      OS Overall Survival    1 127.97439284 #> 142    xx142   A  34.0826279  DAYS      OS Overall Survival    1  34.08262794 #> 143    xx143   A  93.3136053  DAYS      OS Overall Survival    1  93.31360530 #> 144    xx144   A  39.6943191  DAYS      OS Overall Survival    1  39.69431912 #> 145    xx145   A 418.5750209  DAYS      OS Overall Survival    0 418.57502086 #> 146    xx146   A 108.9082910  DAYS      OS Overall Survival    1 108.90829102 #> 147    xx147   A 105.0762188  DAYS      OS Overall Survival    0 105.07621880 #> 148    xx148   A  56.6565761  DAYS      OS Overall Survival    0  56.65657615 #> 149    xx149   A  80.5380440  DAYS      OS Overall Survival    0  80.53804397 #> 150    xx150   A 142.4839370  DAYS      OS Overall Survival    1 142.48393705 #> 151    xx151   A 189.7859562  DAYS      OS Overall Survival    0 189.78595616 #> 152    xx152   A  44.5140716  DAYS      OS Overall Survival    1  44.51407157 #> 153    xx153   A  30.8273271  DAYS      OS 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124.88279256 #> 166    xx166   A  66.0733700  DAYS      OS Overall Survival    1  66.07336995 #> 167    xx167   A  18.0631542  DAYS      OS Overall Survival    1  18.06315423 #> 168    xx168   A  35.7879285  DAYS      OS Overall Survival    1  35.78792846 #> 169    xx169   A  35.6976157  DAYS      OS Overall Survival    0  35.69761571 #> 170    xx170   A 344.6852715  DAYS      OS Overall Survival    1 344.68527146 #> 171    xx171   A 130.7031601  DAYS      OS Overall Survival    1 130.70316012 #> 172    xx172   A  75.4758943  DAYS      OS Overall Survival    0  75.47589431 #> 173    xx173   A  79.9076509  DAYS      OS Overall Survival    1  79.90765094 #> 174    xx174   A   3.8751813  DAYS      OS Overall Survival    1   3.87518134 #> 175    xx175   A  56.6804001  DAYS      OS Overall Survival    1  56.68040006 #> 176    xx176   A  88.0871919  DAYS      OS Overall Survival    1  88.08719189 #> 177    xx177   A  40.1599769  DAYS      OS Overall Survival    1  40.15997687 #> 178    xx178 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66.04417641 #> 203    xx203   A 329.8200403  DAYS      OS Overall Survival    1 329.82004030 #> 204    xx204   A 130.9970045  DAYS      OS Overall Survival    0 130.99700454 #> 205    xx205   A  32.8199139  DAYS      OS Overall Survival    1  32.81991390 #> 206    xx206   A 111.9904588  DAYS      OS Overall Survival    0 111.99045884 #> 207    xx207   A 259.3829166  DAYS      OS Overall Survival    1 259.38291664 #> 208    xx208   A  48.0823337  DAYS      OS Overall Survival    1  48.08233367 #> 209    xx209   A 373.8745493  DAYS      OS Overall Survival    1 373.87454926 #> 210    xx210   A 500.0000000  DAYS      OS Overall Survival    1 500.00000000 #> 211    xx211   A  25.5794699  DAYS      OS Overall Survival    0  25.57946992 #> 212    xx212   A 362.2067038  DAYS      OS Overall Survival    0 362.20670383 #> 213    xx213   A  49.2037680  DAYS      OS Overall Survival    0  49.20376800 #> 214    xx214   A  12.3885075  DAYS      OS Overall Survival    1  12.38850749 #> 215    xx215   A  71.2672138  DAYS      OS Overall Survival    1  71.26721381 #> 216    xx216   A   3.3942755  DAYS      OS Overall Survival    0   3.39427545 #> 217    xx217   A  34.0987472  DAYS      OS Overall Survival    1  34.09874722 #> 218    xx218   A  91.9824456  DAYS      OS Overall Survival    0  91.98244560 #> 219    xx219   A 320.7036093  DAYS      OS Overall Survival    0 320.70360935 #> 220    xx220   A 178.5031223  DAYS      OS Overall Survival    1 178.50312226 #> 221    xx221   A 116.0163469  DAYS      OS Overall Survival    1 116.01634691 #> 222    xx222   A 134.5847123  DAYS      OS Overall Survival    1 134.58471230 #> 223    xx223   A 246.1299544  DAYS      OS Overall Survival    1 246.12995441 #> 224    xx224   A 132.8690671  DAYS      OS Overall Survival    1 132.86906709 #> 225    xx225   A  25.3781343  DAYS      OS Overall Survival    1  25.37813425 #> 226    xx226   A 180.2837518  DAYS      OS Overall Survival    1 180.28375183 #> 227    xx227   A  12.8976681  DAYS      OS Overall Survival    0  12.89766811 #> 228    xx228   A  48.7539111  DAYS      OS Overall Survival    0  48.75391109 #> 229    xx229   A 254.5446271  DAYS      OS Overall Survival    0 254.54462707 #> 230    xx230   A   8.3675931  DAYS      OS Overall Survival    0   8.36759307 #> 231    xx231   A  30.4157094  DAYS      OS Overall Survival    0  30.41570943 #> 232    xx232   A  32.3195134  DAYS      OS Overall Survival    1  32.31951339 #> 233    xx233   A   5.8283381  DAYS      OS Overall Survival    0   5.82833812 #> 234    xx234   A 409.5208072  DAYS      OS Overall Survival    1 409.52080716 #> 235    xx235   A  80.7172811  DAYS      OS Overall Survival    1  80.71728110 #> 236    xx236   A  29.5736758  DAYS      OS Overall Survival    1  29.57367581 #> 237    xx237   A  88.3243550  DAYS      OS Overall Survival    0  88.32435496 #> 238    xx238   A  16.6777043  DAYS      OS Overall Survival    0  16.67770427 #> 239    xx239   A 201.8084701  DAYS      OS Overall Survival    0 201.80847006 #> 240    xx240   A 310.0655345  DAYS      OS Overall Survival    1 310.06553454 #> 241    xx241   A   4.0793628  DAYS      OS Overall Survival    1   4.07936281 #> 242    xx242   A   1.2688996  DAYS      OS Overall Survival    0   1.26889959 #> 243    xx243   A  33.3162513  DAYS      OS Overall Survival    1  33.31625135 #> 244    xx244   A  70.2602449  DAYS      OS Overall Survival    1  70.26024492 #> 245    xx245   A   5.4278376  DAYS      OS Overall Survival    1   5.42783756 #> 246    xx246   A  50.1248807  DAYS      OS Overall Survival    1  50.12488066 #> 247    xx247   A  85.1873869  DAYS      OS Overall Survival    1  85.18738687 #> 248    xx248   A 464.9161960  DAYS      OS Overall Survival    0 464.91619598 #> 249    xx249   A  26.3512119  DAYS      OS Overall Survival    1  26.35121187 #> 250    xx250   A  23.2462453  DAYS      OS Overall Survival    0  23.24624530 #> 251    xx251   A  54.6123914  DAYS      OS Overall Survival    1  54.61239143 #> 252    xx252   A 204.7131962  DAYS      OS Overall Survival    1 204.71319623 #> 253    xx253   A 221.0842591  DAYS      OS Overall Survival    0 221.08425909 #> 254    xx254   A 126.8931381  DAYS      OS Overall Survival    1 126.89313809 #> 255    xx255   A   6.8416124  DAYS      OS Overall Survival    0   6.84161237 #> 256    xx256   A  39.5099966  DAYS      OS Overall Survival    1  39.50999657 #> 257    xx257   A 303.3503769  DAYS      OS Overall Survival    1 303.35037691 #> 258    xx258   A 103.3599337  DAYS      OS Overall Survival    1 103.35993372 #> 259    xx259   A  73.0774123  DAYS      OS Overall Survival    1  73.07741226 #> 260    xx260   A  15.2886098  DAYS      OS Overall Survival    1  15.28860979 #> 261    xx261   A 144.5084460  DAYS      OS Overall Survival    0 144.50844597 #> 262    xx262   A 227.9757161  DAYS      OS Overall Survival    1 227.97571613 #> 263    xx263   A 233.3291179  DAYS      OS Overall Survival    1 233.32911792 #> 264    xx264   A  10.9352310  DAYS      OS Overall Survival    1  10.93523101 #> 265    xx265   A 284.9961410  DAYS      OS Overall Survival    1 284.99614097 #> 266    xx266   A 119.6814391  DAYS      OS Overall Survival    1 119.68143912 #> 267    xx267   A 153.7818843  DAYS      OS Overall Survival    1 153.78188428 #> 268    xx268   A  41.5788383  DAYS      OS Overall Survival    1  41.57883829 #> 269    xx269   A  17.1922456  DAYS      OS Overall Survival    1  17.19224560 #> 270    xx270   A  12.1850070  DAYS      OS Overall Survival    1  12.18500697 #> 271    xx271   A 301.7347889  DAYS      OS Overall Survival    1 301.73478886 #> 272    xx272   A  93.7104093  DAYS      OS Overall Survival    1  93.71040927 #> 273    xx273   A 414.9602508  DAYS      OS Overall Survival    0 414.96025076 #> 274    xx274   A 203.2206009  DAYS      OS Overall Survival    1 203.22060092 #> 275    xx275   A  81.0661957  DAYS      OS Overall Survival    1  81.06619570 #> 276    xx276   A 135.8094446  DAYS      OS Overall Survival    0 135.80944463 #> 277    xx277   A 113.1377720  DAYS      OS Overall Survival    1 113.13777204 #> 278    xx278   A 406.0771660  DAYS      OS Overall Survival    1 406.07716604 #> 279    xx279   A 136.1992022  DAYS      OS Overall Survival    0 136.19920218 #> 280    xx280   A 110.6752035  DAYS      OS Overall Survival    1 110.67520350 #> 281    xx281   A 282.6318510  DAYS      OS Overall Survival    1 282.63185100 #> 282    xx282   A 245.5501089  DAYS      OS Overall Survival    1 245.55010890 #> 283    xx283   A 101.3714715  DAYS      OS Overall Survival    1 101.37147148 #> 284    xx284   A  14.6109023  DAYS      OS Overall Survival    0  14.61090228 #> 285    xx285   A   3.2894058  DAYS      OS Overall Survival    1   3.28940576 #> 286    xx286   A  20.4779808  DAYS      OS Overall Survival    1  20.47798081 #> 287    xx287   A   0.3321626  DAYS      OS Overall Survival    0   0.33216257 #> 288    xx288   A 178.9681939  DAYS      OS Overall Survival    1 178.96819391 #> 289    xx289 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OS Overall Survival    1 204.60858275 #> 302    xx302   A  34.5820833  DAYS      OS Overall Survival    1  34.58208330 #> 303    xx303   A  37.8814246  DAYS      OS Overall Survival    1  37.88142464 #> 304    xx304   A  88.6145506  DAYS      OS Overall Survival    1  88.61455059 #> 305    xx305   A  98.5382896  DAYS      OS Overall Survival    0  98.53828958 #> 306    xx306   A 237.8774307  DAYS      OS Overall Survival    0 237.87743067 #> 307    xx307   A  76.6524844  DAYS      OS Overall Survival    0  76.65248445 #> 308    xx308   A  91.8647559  DAYS      OS Overall Survival    1  91.86475594 #> 309    xx309   A  51.6937890  DAYS      OS Overall Survival    1  51.69378901 #> 310    xx310   A  29.5678669  DAYS      OS Overall Survival    1  29.56786685 #> 311    xx311   A  71.2587985  DAYS      OS Overall Survival    0  71.25879852 #> 312    xx312   A 145.2570428  DAYS      OS Overall Survival    0 145.25704283 #> 313    xx313   A 249.9233069  DAYS      OS Overall Survival    1 249.92330689 #> 314    xx314   A 131.4908055  DAYS      OS Overall Survival    0 131.49080545 #> 315    xx315   A 312.7128096  DAYS      OS Overall Survival    0 312.71280965 #> 316    xx316   A  22.6567004  DAYS      OS Overall Survival    0  22.65670039 #> 317    xx317   A  96.7897325  DAYS      OS Overall Survival    1  96.78973248 #> 318    xx318   A 103.3858997  DAYS      OS Overall Survival    0 103.38589969 #> 319    xx319   A  34.5806822  DAYS      OS Overall Survival    0  34.58068220 #> 320    xx320   A  56.7837124  DAYS      OS Overall Survival    1  56.78371243 #> 321    xx321   A 311.0184998  DAYS      OS Overall Survival    1 311.01849975 #> 322    xx322   A  63.9452407  DAYS      OS Overall Survival    1  63.94524074 #> 323    xx323   A 173.7507167  DAYS      OS Overall Survival    1 173.75071665 #> 324    xx324   A   4.6245131  DAYS      OS Overall Survival    1   4.62451308 #> 325    xx325   A  52.2469200  DAYS      OS Overall Survival    0  52.24691999 #> 326    xx326   A  93.2408592  DAYS      OS Overall Survival    1  93.24085921 #> 327    xx327   A 101.6701105  DAYS      OS Overall Survival    1 101.67011046 #> 328    xx328   A 247.7760192  DAYS      OS Overall Survival    1 247.77601921 #> 329    xx329   A  43.3221132  DAYS      OS Overall Survival    1  43.32211317 #> 330    xx330   A 130.9639163  DAYS      OS Overall Survival    1 130.96391627 #> 331    xx331   A  89.7755695  DAYS      OS Overall Survival    0  89.77556948 #> 332    xx332   A  37.7626359  DAYS      OS Overall Survival    0  37.76263590 #> 333    xx333   A 236.1039880  DAYS      OS Overall Survival    1 236.10398798 #> 334    xx334   A   5.5735189  DAYS      OS Overall Survival    1   5.57351887 #> 335    xx335   A  15.9789179  DAYS      OS Overall Survival    1  15.97891788 #> 336    xx336   A 219.5385758  DAYS      OS Overall Survival    1 219.53857583 #> 337    xx337   A 122.1766793  DAYS      OS Overall Survival    1 122.17667929 #> 338    xx338   A  85.2629108  DAYS      OS Overall Survival    0  85.26291085 #> 339    xx339   A  72.1670941  DAYS      OS Overall Survival    0  72.16709408 #> 340    xx340   A  49.3435304  DAYS      OS Overall Survival    0  49.34353036 #> 341    xx341   A  84.3199651  DAYS      OS Overall Survival    1  84.31996511 #> 342    xx342   A 102.0646836  DAYS      OS Overall Survival    1 102.06468356 #> 343    xx343   A  99.9203054  DAYS      OS Overall Survival    1  99.92030535 #> 344    xx344   A 105.9591180  DAYS      OS Overall Survival    1 105.95911796 #> 345    xx345   A 341.3657643  DAYS      OS Overall Survival    1 341.36576428 #> 346    xx346   A  61.1170188  DAYS      OS Overall Survival    0  61.11701880 #> 347    xx347   A 273.1257002  DAYS      OS Overall Survival    1 273.12570021 #> 348    xx348   A  83.7537322  DAYS      OS Overall Survival    1  83.75373223 #> 349    xx349   A 287.8308610  DAYS      OS Overall Survival    1 287.83086098 #> 350    xx350   A  85.1705400  DAYS      OS Overall Survival    1  85.17054003 #> 351    xx351   A 500.0000000  DAYS      OS Overall Survival    1 500.00000000 #> 352    xx352   A  60.6304562  DAYS      OS Overall Survival    1  60.63045617 #> 353    xx353   A 405.1024439  DAYS      OS Overall Survival    1 405.10244392 #> 354    xx354   A  10.3618326  DAYS      OS Overall Survival    1  10.36183257 #> 355    xx355   A  13.8586577  DAYS      OS Overall Survival    1  13.85865767 #> 356    xx356   A 222.7575312  DAYS      OS Overall Survival    1 222.75753120 #> 357    xx357   A  12.9398765  DAYS      OS Overall Survival    1  12.93987648 #> 358    xx358   A  71.8214219  DAYS      OS Overall Survival    1  71.82142185 #> 359    xx359   A  28.0451023  DAYS      OS Overall Survival    0  28.04510230 #> 360    xx360   A 217.0832170  DAYS      OS Overall Survival    0 217.08321700 #> 361    xx361   A  30.4968240  DAYS      OS Overall Survival    0  30.49682405 #> 362    xx362   A  37.0950110  DAYS      OS Overall Survival    1  37.09501101 #> 363    xx363   A  15.8151577  DAYS      OS Overall Survival    1  15.81515774 #> 364    xx364   A   9.1691822  DAYS      OS Overall Survival    1   9.16918216 #> 365    xx365   A 128.8321170  DAYS      OS Overall Survival    0 128.83211696 #> 366    xx366   A  68.7223284  DAYS      OS Overall Survival    1  68.72232845 #> 367    xx367   A  23.2247571  DAYS      OS Overall Survival    1  23.22475713 #> 368    xx368   A 500.0000000  DAYS      OS Overall Survival    1 500.00000000 #> 369    xx369   A  12.0051551  DAYS      OS Overall Survival    0  12.00515506 #> 370    xx370   A  70.5995378  DAYS      OS Overall Survival    1  70.59953776 #> 371    xx371   A 128.1287128  DAYS      OS Overall Survival    1 128.12871283 #> 372    xx372   A 173.8815563  DAYS      OS Overall Survival    1 173.88155626 #> 373    xx373   A 147.5909236  DAYS      OS Overall Survival    0 147.59092358 #> 374    xx374   A 106.7080111  DAYS      OS Overall Survival    1 106.70801109 #> 375    xx375   A  72.1695415  DAYS      OS Overall Survival    0  72.16954153 #> 376    xx376   A 100.0319960  DAYS      OS Overall Survival    1 100.03199602 #> 377    xx377   A  21.5799492  DAYS      OS Overall Survival    0  21.57994924 #> 378    xx378   A 129.3533244  DAYS      OS Overall Survival    1 129.35332437 #> 379    xx379   A   1.9459751  DAYS      OS Overall Survival    0   1.94597508 #> 380    xx380   A  50.8566442  DAYS      OS Overall Survival    0  50.85664425 #> 381    xx381   A 169.4422522  DAYS      OS Overall Survival    0 169.44225216 #> 382    xx382   A 141.6016337  DAYS      OS Overall Survival    1 141.60163373 #> 383    xx383   A 500.0000000  DAYS      OS Overall Survival    1 500.00000000 #> 384    xx384   A  67.3416893  DAYS      OS Overall Survival    0  67.34168933 #> 385    xx385   A 293.6815759  DAYS      OS Overall Survival    0 293.68157592 #> 386    xx386   A 170.5776237  DAYS      OS Overall Survival    0 170.57762370 #> 387    xx387   A  74.4971040  DAYS      OS Overall Survival    1  74.49710397 #> 388    xx388   A 252.7425757  DAYS      OS Overall Survival    1 252.74257574 #> 389    xx389   A 370.3157123  DAYS      OS Overall Survival    0 370.31571227 #> 390    xx390   A  18.4767328  DAYS      OS Overall Survival    1  18.47673282 #> 391    xx391   A 242.3122311  DAYS      OS Overall Survival    1 242.31223115 #> 392    xx392   A 108.6286686  DAYS      OS Overall Survival    1 108.62866865 #> 393    xx393   A 135.1588000  DAYS      OS Overall Survival    0 135.15879995 #> 394    xx394   A  13.4000154  DAYS      OS Overall Survival    0  13.40001542 #> 395    xx395   A  50.6666237  DAYS      OS Overall Survival    0  50.66662375 #> 396    xx396   A 181.3283312  DAYS      OS Overall Survival    1 181.32833115 #> 397    xx397   A  13.0073322  DAYS      OS Overall Survival    1  13.00733220 #> 398    xx398   A 357.7553493  DAYS      OS Overall Survival    1 357.75534926 #> 399    xx399   A 180.9009377  DAYS      OS Overall Survival    0 180.90093774 #> 400    xx400   A 242.1178978  DAYS      OS Overall Survival    1 242.11789784 #> 401    xx401   A  60.2893293  DAYS      OS Overall Survival    1  60.28932929 #> 402    xx402   A  93.4905863  DAYS      OS Overall Survival    1  93.49058627 #> 403    xx403   A  52.7997845  DAYS      OS Overall Survival    0  52.79978448 #> 404    xx404   A 350.2654152  DAYS      OS Overall Survival    1 350.26541519 #> 405    xx405   A 241.1774392  DAYS      OS Overall Survival    1 241.17743919 #> 406    xx406   A   8.7683064  DAYS      OS Overall Survival    1   8.76830642 #> 407    xx407   A 330.7110893  DAYS      OS Overall Survival    1 330.71108928 #> 408    xx408   A  48.4040249  DAYS      OS Overall Survival    1  48.40402492 #> 409    xx409   A  77.9263829  DAYS      OS Overall Survival    1  77.92638291 #> 410    xx410   A  16.1304526  DAYS      OS Overall Survival    1  16.13045259 #> 411    xx411   A   9.3282041  DAYS      OS Overall Survival    1   9.32820410 #> 412    xx412   A 164.7372721  DAYS      OS 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318.78330172 #> 425    xx425   A 359.1658176  DAYS      OS Overall Survival    1 359.16581755 #> 426    xx426   A  14.1516358  DAYS      OS Overall Survival    0  14.15163577 #> 427    xx427   A 104.8671198  DAYS      OS Overall Survival    1 104.86711984 #> 428    xx428   A 158.0674152  DAYS      OS Overall Survival    1 158.06741518 #> 429    xx429   A  53.4114635  DAYS      OS Overall Survival    0  53.41146353 #> 430    xx430   A  52.6613128  DAYS      OS Overall Survival    0  52.66131277 #> 431    xx431   A 402.2578995  DAYS      OS Overall Survival    1 402.25789952 #> 432    xx432   A   2.4121713  DAYS      OS Overall Survival    1   2.41217134 #> 433    xx433   A 189.4981770  DAYS      OS Overall Survival    0 189.49817697 #> 434    xx434   A   4.2866692  DAYS      OS Overall Survival    1   4.28666924 #> 435    xx435   A 256.4404196  DAYS      OS Overall Survival    1 256.44041960 #> 436    xx436   A 452.7208595  DAYS      OS Overall Survival    0 452.72085955 #> 437    xx437 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OS Overall Survival    1 245.88575171 #> 487    xx487   A 117.1680451  DAYS      OS Overall Survival    0 117.16804508 #> 488    xx488   A 143.3116393  DAYS      OS Overall Survival    0 143.31163931 #> 489    xx489   A 382.6979071  DAYS      OS Overall Survival    0 382.69790711 #> 490    xx490   A  33.8249243  DAYS      OS Overall Survival    1  33.82492430 #> 491    xx491   A 157.0907872  DAYS      OS Overall Survival    1 157.09078722 #> 492    xx492   A  15.7821636  DAYS      OS Overall Survival    0  15.78216361 #> 493    xx493   A  87.0820950  DAYS      OS Overall Survival    0  87.08209504 #> 494    xx494   A 190.6912802  DAYS      OS Overall Survival    0 190.69128020 #> 495    xx495   A  49.8173522  DAYS      OS Overall Survival    1  49.81735221 #> 496    xx496   A 215.4027340  DAYS      OS Overall Survival    0 215.40273403 #> 497    xx497   A 111.1416159  DAYS      OS Overall Survival    0 111.14161594 #> 498    xx498   A  17.0752816  DAYS      OS Overall Survival    0  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206.61783178 #> 573   yyxx73   C 379.0575377  DAYS      OS Overall Survival    1   0.91976008 #> 574   yyxx74   C 295.6642687  DAYS      OS Overall Survival    1 131.53609616 #> 575   yyxx75   C 180.0471671  DAYS      OS Overall Survival    0   6.67270070 #> 576   yyxx76   C  88.3276166  DAYS      OS Overall Survival    1  56.68002422 #> 577   yyxx77   C 230.9483853  DAYS      OS Overall Survival    0  37.46982564 #> 578   yyxx78   C  19.5843537  DAYS      OS Overall Survival    1   4.93925634 #> 579   yyxx79   C  67.6855902  DAYS      OS Overall Survival    1  30.39641299 #> 580   yyxx80   C 187.2930109  DAYS      OS Overall Survival    0  89.62338826 #> 581   yyxx81   C 205.1596244  DAYS      OS Overall Survival    1 248.63747470 #> 582   yyxx82   C 133.9676829  DAYS      OS Overall Survival    1  49.12069715 #> 583   yyxx83   C  99.3645367  DAYS      OS Overall Survival    1  11.96292668 #> 584   yyxx84   C 152.3036359  DAYS      OS Overall Survival    0  61.14344746 #> 585   yyxx85 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66.82508584 #> 906  yyxx406   C   8.7683064  DAYS      OS Overall Survival    1  16.86874477 #> 907  yyxx407   C 330.7110893  DAYS      OS Overall Survival    1 120.50313158 #> 908  yyxx408   C  48.4040249  DAYS      OS Overall Survival    1   6.92270204 #> 909  yyxx409   C  77.9263829  DAYS      OS Overall Survival    1  77.58478458 #> 910  yyxx410   C  16.1304526  DAYS      OS Overall Survival    1  41.37153225 #> 911  yyxx411   C   9.3282041  DAYS      OS Overall Survival    1 128.46602893 #> 912  yyxx412   C 164.7372721  DAYS      OS Overall Survival    0 144.00685556 #> 913  yyxx413   C 366.6874542  DAYS      OS Overall Survival    0 343.19641239 #> 914  yyxx414   C  38.5668907  DAYS      OS Overall Survival    1  19.70864700 #> 915  yyxx415   C  21.8873529  DAYS      OS Overall Survival    1 183.05948366 #> 916  yyxx416   C  20.9492488  DAYS      OS Overall Survival    1  41.50064484 #> 917  yyxx417   C 174.2766212  DAYS      OS Overall Survival    0  55.63454118 #> 918  yyxx418   C  57.1719156  DAYS      OS Overall Survival    1 127.75039430 #> 919  yyxx419   C  41.6267738  DAYS      OS Overall Survival    0  10.89958404 #> 920  yyxx420   C  15.8302898  DAYS      OS Overall Survival    0  40.86016804 #> 921  yyxx421   C  86.8743175  DAYS      OS Overall Survival    0  87.81394704 #> 922  yyxx422   C 350.3644114  DAYS      OS Overall Survival    1 223.19097765 #> 923  yyxx423   C 241.5604544  DAYS      OS Overall Survival    0  32.95351409 #> 924  yyxx424   C 318.7833017  DAYS      OS Overall Survival    0   8.11811192 #> 925  yyxx425   C 359.1658176  DAYS      OS Overall Survival    1 258.22347628 #> 926  yyxx426   C  14.1516358  DAYS      OS Overall Survival    0  18.04976559 #> 927  yyxx427   C 104.8671198  DAYS      OS Overall Survival    1   3.65964496 #> 928  yyxx428   C 158.0674152  DAYS      OS Overall Survival    1  42.66199303 #> 929  yyxx429   C  53.4114635  DAYS      OS Overall Survival    0  32.33421493 #> 930  yyxx430   C  52.6613128  DAYS      OS Overall Survival    0  25.75751268 #> 931  yyxx431   C 402.2578995  DAYS      OS Overall Survival    1  88.21501732 #> 932  yyxx432   C   2.4121713  DAYS      OS Overall Survival    1 131.90296010 #> 933  yyxx433   C 189.4981770  DAYS      OS Overall Survival    0  44.52073874 #> 934  yyxx434   C   4.2866692  DAYS      OS Overall Survival    1 254.86526980 #> 935  yyxx435   C 256.4404196  DAYS      OS Overall Survival    1  25.41412967 #> 936  yyxx436   C 452.7208595  DAYS      OS Overall Survival    0  86.98203003 #> 937  yyxx437   C  31.7437313  DAYS      OS Overall Survival    1  37.00076281 #> 938  yyxx438   C 370.0496553  DAYS      OS Overall Survival    1 263.48863428 #> 939  yyxx439   C 299.0591141  DAYS      OS Overall Survival    1 111.49580995 #> 940  yyxx440   C 134.9211996  DAYS      OS Overall Survival    1  70.26925420 #> 941  yyxx441   C  34.0150132  DAYS      OS Overall Survival    0 147.78736954 #> 942  yyxx442   C  36.3408479  DAYS      OS Overall Survival    0 196.43090995 #> 943  yyxx443   C 133.8500312  DAYS      OS Overall Survival    1 115.31218620 #> 944  yyxx444   C 332.3420058  DAYS      OS Overall Survival    1  53.77638968 #> 945  yyxx445   C  23.7400288  DAYS      OS Overall Survival    1 471.44570903 #> 946  yyxx446   C 343.7001138  DAYS      OS Overall Survival    0  63.21296363 #> 947  yyxx447   C 222.9677203  DAYS      OS Overall Survival    1 193.94676890 #> 948  yyxx448   C  76.9672003  DAYS      OS Overall Survival    0  79.54939426 #> 949  yyxx449   C 389.5516777  DAYS      OS Overall Survival    0  75.42852758 #> 950  yyxx450   C 174.7162274  DAYS      OS Overall Survival    1   2.17200838 #> 951  yyxx451   C 363.8263817  DAYS      OS Overall Survival    0  23.64819180 #> 952  yyxx452   C  19.6747055  DAYS      OS Overall Survival    0 128.21885514 #> 953  yyxx453   C  19.9823259  DAYS      OS Overall Survival    1 500.00000000 #> 954  yyxx454   C  10.2292442  DAYS      OS Overall Survival    1 319.62380090 #> 955  yyxx455   C 326.7511055  DAYS      OS Overall Survival    1 117.49511217 #> 956  yyxx456   C  20.0770245  DAYS      OS Overall Survival    0  93.11604033 #> 957  yyxx457   C  36.0369583  DAYS      OS Overall Survival    0  22.86049975 #> 958  yyxx458   C 184.0466008  DAYS      OS Overall Survival    0  73.90224221 #> 959  yyxx459   C  76.6013852  DAYS      OS Overall Survival    0  66.90302316 #> 960  yyxx460   C 207.4070734  DAYS      OS Overall Survival    1   3.32411004 #> 961  yyxx461   C 154.3107802  DAYS      OS Overall Survival    1  36.13140895 #> 962  yyxx462   C 106.6420898  DAYS      OS Overall Survival    0 208.62293514 #> 963  yyxx463   C 205.0363326  DAYS      OS Overall Survival    0  38.36725822 #> 964  yyxx464   C  20.9126041  DAYS      OS Overall Survival    0  56.29689086 #> 965  yyxx465   C  18.6336849  DAYS      OS Overall Survival    1  15.44590687 #> 966  yyxx466   C 136.3800132  DAYS      OS Overall Survival    0  17.28062264 #> 967  yyxx467   C 316.3884586  DAYS      OS Overall Survival    1  61.55896497 #> 968  yyxx468   C 141.3017821  DAYS      OS Overall Survival    1  38.53035418 #> 969  yyxx469   C  13.2112969  DAYS      OS Overall Survival    1  78.26857427 #> 970  yyxx470   C  99.6448581  DAYS      OS Overall Survival    1  66.54501610 #> 971  yyxx471   C 135.2069709  DAYS      OS Overall Survival    0  11.75128101 #> 972  yyxx472   C  61.6891086  DAYS      OS Overall Survival    1  27.14682548 #> 973  yyxx473   C 101.1286638  DAYS      OS Overall Survival    1   0.04489938 #> 974  yyxx474   C 166.2426128  DAYS      OS Overall Survival    0 306.36501330 #> 975  yyxx475   C  79.8316941  DAYS      OS Overall Survival    1  39.29798097 #> 976  yyxx476   C 234.7546470  DAYS      OS Overall Survival    1 385.93370361 #> 977  yyxx477   C  94.7052270  DAYS      OS Overall Survival    1  55.42401529 #> 978  yyxx478   C 168.1161018  DAYS      OS Overall Survival    0  48.41662491 #> 979  yyxx479   C 146.5126322  DAYS      OS Overall Survival    1 107.22939825 #> 980  yyxx480   C 500.0000000  DAYS      OS Overall Survival    1  11.90545016 #> 981  yyxx481   C 130.1803275  DAYS      OS Overall Survival    1  57.43646612 #> 982  yyxx482   C  39.2364700  DAYS      OS Overall Survival    0  64.20765604 #> 983  yyxx483   C  67.4707314  DAYS      OS Overall Survival    0  87.34293902 #> 984  yyxx484   C  89.4307290  DAYS      OS Overall Survival    0  36.06745045 #> 985  yyxx485   C 101.4314849  DAYS      OS Overall Survival    1  11.27916376 #> 986  yyxx486   C 245.8857517  DAYS      OS Overall Survival    1   5.25986944 #> 987  yyxx487   C 117.1680451  DAYS      OS Overall Survival    0 121.86844361 #> 988  yyxx488   C 143.3116393  DAYS      OS Overall Survival    0 100.05926345 #> 989  yyxx489   C 382.6979071  DAYS      OS Overall Survival    0 122.88430454 #> 990  yyxx490   C  33.8249243  DAYS      OS Overall Survival    1  13.50143102 #> 991  yyxx491   C 157.0907872  DAYS      OS Overall Survival    1  72.28518986 #> 992  yyxx492   C  15.7821636  DAYS      OS Overall Survival    0 122.64731604 #> 993  yyxx493   C  87.0820950  DAYS      OS Overall Survival    0 229.33116122 #> 994  yyxx494   C 190.6912802  DAYS      OS Overall Survival    0  29.07607607 #> 995  yyxx495   C  49.8173522  DAYS      OS Overall Survival    1  86.44606009 #> 996  yyxx496   C 215.4027340  DAYS      OS Overall Survival    0  32.22703111 #> 997  yyxx497   C 111.1416159  DAYS      OS Overall Survival    0  73.37981988 #> 998  yyxx498   C  17.0752816  DAYS      OS Overall Survival    0   6.13227059 #> 999  yyxx499   C  98.2021703  DAYS      OS Overall Survival    0  73.69939882 #> 1000 yyxx500   C  97.5095606  DAYS      OS Overall Survival    0  71.52985288 #>      EVENT #> 1        0 #> 2        0 #> 3        0 #> 4        0 #> 5        0 #> 6        1 #> 7        0 #> 8        1 #> 9        0 #> 10       0 #> 11       0 #> 12       0 #> 13       1 #> 14       1 #> 15       0 #> 16       0 #> 17       0 #> 18       1 #> 19       0 #> 20   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#> 92       1 #> 93       0 #> 94       0 #> 95       1 #> 96       0 #> 97       0 #> 98       1 #> 99       1 #> 100      0 #> 101      0 #> 102      1 #> 103      0 #> 104      0 #> 105      0 #> 106      1 #> 107      1 #> 108      0 #> 109      1 #> 110      0 #> 111      0 #> 112      0 #> 113      0 #> 114      0 #> 115      1 #> 116      1 #> 117      0 #> 118      0 #> 119      1 #> 120      0 #> 121      0 #> 122      0 #> 123      0 #> 124      1 #> 125      1 #> 126      0 #> 127      0 #> 128      0 #> 129      1 #> 130      0 #> 131      0 #> 132      0 #> 133      1 #> 134      1 #> 135      0 #> 136      0 #> 137      0 #> 138      0 #> 139      1 #> 140      0 #> 141      0 #> 142      0 #> 143      0 #> 144      0 #> 145      1 #> 146      0 #> 147      1 #> 148      1 #> 149      1 #> 150      0 #> 151      1 #> 152      0 #> 153      1 #> 154      1 #> 155      0 #> 156      0 #> 157      0 #> 158      0 #> 159      1 #> 160      0 #> 161      1 #> 162      0 #> 163 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#> 235      0 #> 236      0 #> 237      1 #> 238      1 #> 239      1 #> 240      0 #> 241      0 #> 242      1 #> 243      0 #> 244      0 #> 245      0 #> 246      0 #> 247      0 #> 248      1 #> 249      0 #> 250      1 #> 251      0 #> 252      0 #> 253      1 #> 254      0 #> 255      1 #> 256      0 #> 257      0 #> 258      0 #> 259      0 #> 260      0 #> 261      1 #> 262      0 #> 263      0 #> 264      0 #> 265      0 #> 266      0 #> 267      0 #> 268      0 #> 269      0 #> 270      0 #> 271      0 #> 272      0 #> 273      1 #> 274      0 #> 275      0 #> 276      1 #> 277      0 #> 278      0 #> 279      1 #> 280      0 #> 281      0 #> 282      0 #> 283      0 #> 284      1 #> 285      0 #> 286      0 #> 287      1 #> 288      0 #> 289      0 #> 290      0 #> 291      1 #> 292      0 #> 293      0 #> 294      0 #> 295      1 #> 296      1 #> 297      1 #> 298      1 #> 299      0 #> 300      0 #> 301      0 #> 302      0 #> 303      0 #> 304      0 #> 305      1 #> 306 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#> 378      0 #> 379      1 #> 380      1 #> 381      1 #> 382      0 #> 383      0 #> 384      1 #> 385      1 #> 386      1 #> 387      0 #> 388      0 #> 389      1 #> 390      0 #> 391      0 #> 392      0 #> 393      1 #> 394      1 #> 395      1 #> 396      0 #> 397      0 #> 398      0 #> 399      1 #> 400      0 #> 401      0 #> 402      0 #> 403      1 #> 404      0 #> 405      0 #> 406      0 #> 407      0 #> 408      0 #> 409      0 #> 410      0 #> 411      0 #> 412      1 #> 413      1 #> 414      0 #> 415      0 #> 416      0 #> 417      1 #> 418      0 #> 419      1 #> 420      1 #> 421      1 #> 422      0 #> 423      1 #> 424      1 #> 425      0 #> 426      1 #> 427      0 #> 428      0 #> 429      1 #> 430      1 #> 431      0 #> 432      0 #> 433      1 #> 434      0 #> 435      0 #> 436      1 #> 437      0 #> 438      0 #> 439      0 #> 440      0 #> 441      1 #> 442      1 #> 443      0 #> 444      0 #> 445      0 #> 446      1 #> 447      0 #> 448      1 #> 449 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     1 #> 736      1 #> 737      1 #> 738      1 #> 739      1 #> 740      1 #> 741      1 #> 742      1 #> 743      1 #> 744      1 #> 745      1 #> 746      1 #> 747      1 #> 748      1 #> 749      1 #> 750      1 #> 751      1 #> 752      1 #> 753      1 #> 754      1 #> 755      1 #> 756      1 #> 757      1 #> 758      1 #> 759      1 #> 760      1 #> 761      1 #> 762      1 #> 763      1 #> 764      1 #> 765      1 #> 766      1 #> 767      1 #> 768      1 #> 769      1 #> 770      1 #> 771      1 #> 772      1 #> 773      1 #> 774      1 #> 775      1 #> 776      1 #> 777      1 #> 778      1 #> 779      1 #> 780      1 #> 781      1 #> 782      1 #> 783      1 #> 784      1 #> 785      1 #> 786      1 #> 787      1 #> 788      1 #> 789      1 #> 790      1 #> 791      1 #> 792      1 #> 793      1 #> 794      1 #> 795      1 #> 796      1 #> 797      1 #> 798      1 #> 799      1 #> 800      1 #> 801      1 #> 802      1 #> 803      1 #> 804      1 #> 805      1 #> 806      1 #> 807      1 #> 808      0 #> 809      1 #> 810      1 #> 811      1 #> 812      1 #> 813      1 #> 814      1 #> 815      1 #> 816      1 #> 817      1 #> 818      1 #> 819      1 #> 820      1 #> 821      1 #> 822      1 #> 823      1 #> 824      1 #> 825      1 #> 826      1 #> 827      1 #> 828      1 #> 829      1 #> 830      1 #> 831      1 #> 832      1 #> 833      1 #> 834      1 #> 835      1 #> 836      1 #> 837      1 #> 838      1 #> 839      1 #> 840      1 #> 841      1 #> 842      1 #> 843      1 #> 844      1 #> 845      1 #> 846      1 #> 847      1 #> 848      1 #> 849      1 #> 850      1 #> 851      1 #> 852      1 #> 853      1 #> 854      1 #> 855      0 #> 856      1 #> 857      1 #> 858      1 #> 859      1 #> 860      1 #> 861      1 #> 862      1 #> 863      1 #> 864      1 #> 865      1 #> 866      1 #> 867      1 #> 868      1 #> 869      1 #> 870      1 #> 871      1 #> 872      1 #> 873      1 #> 874      1 #> 875      1 #> 876      1 #> 877      1 #> 878      1 #> 879      1 #> 880      1 #> 881      1 #> 882      1 #> 883      1 #> 884      1 #> 885      1 #> 886      1 #> 887      1 #> 888      1 #> 889      1 #> 890      1 #> 891      1 #> 892      1 #> 893      1 #> 894      1 #> 895      1 #> 896      1 #> 897      1 #> 898      1 #> 899      1 #> 900      1 #> 901      1 #> 902      1 #> 903      1 #> 904      1 #> 905      1 #> 906      1 #> 907      1 #> 908      1 #> 909      1 #> 910      1 #> 911      1 #> 912      1 #> 913      1 #> 914      1 #> 915      1 #> 916      1 #> 917      1 #> 918      1 #> 919      1 #> 920      1 #> 921      1 #> 922      1 #> 923      1 #> 924      1 #> 925      1 #> 926      1 #> 927      1 #> 928      1 #> 929      1 #> 930      1 #> 931      1 #> 932      1 #> 933      1 #> 934      1 #> 935      1 #> 936      1 #> 937      1 #> 938      1 #> 939      1 #> 940      1 #> 941      1 #> 942      1 #> 943      1 #> 944      1 #> 945      1 #> 946      1 #> 947      1 #> 948      1 #> 949      1 #> 950      1 #> 951      1 #> 952      1 #> 953      0 #> 954      1 #> 955      1 #> 956      1 #> 957      1 #> 958      1 #> 959      1 #> 960      1 #> 961      1 #> 962      1 #> 963      1 #> 964      1 #> 965      1 #> 966      1 #> 967      1 #> 968      1 #> 969      1 #> 970      1 #> 971      1 #> 972      1 #> 973      1 #> 974      1 #> 975      1 #> 976      1 #> 977      1 #> 978      1 #> 979      1 #> 980      1 #> 981      1 #> 982      1 #> 983      1 #> 984      1 #> 985      1 #> 986      1 #> 987      1 #> 988      1 #> 989      1 #> 990      1 #> 991      1 #> 992      1 #> 993      1 #> 994      1 #> 995      1 #> 996      1 #> 997      1 #> 998      1 #> 999      1 #> 1000     1  ### AgD # Baseline aggregate data for the comparator population agd #>        STUDY          ARM   N AGE_MEAN AGE_MEDIAN AGE_SD SEX_MALE_COUNT #> 1 Study_XXXX        Total 300       51         49   3.25            147 #> 2 Study_XXXX Intervention  NA       NA         NA     NA             NA #> 3 Study_XXXX   Comparator  NA       NA         NA     NA             NA #>   ECOG0_COUNT SMOKE_COUNT N_PR_THER_MEDIAN #> 1         105          58                2 #> 2          NA          NA               NA #> 3          NA          NA               NA  # for time-to-event endpoints, pseudo IPD from digitalized KM pseudo_ipd_twt #>            Time Event ARM #> 1    20.2311676     1   B #> 2    28.7679537     1   B #> 3    41.0662129     0   B #> 4     0.8492261     1   B #> 5     9.0521882     0   B #> 6     3.4450075     1   B #> 7    88.7049039     0   B #> 8    75.5205919     0   B #> 9   102.0480708     0   B #> 10   61.1832674     0   B #> 11   44.6248777     1   B #> 12  417.7060892     0   B #> 13  188.0853029     1   B #> 14  135.4086771     1   B #> 15   14.4693075     1   B #> 16   19.2886739     1   B #> 17  253.9413828     0   B #> 18   18.8265045     1   B #> 19   42.5311133     0   B #> 20  203.2845550     0   B #> 21   32.2845542     1   B #> 22    3.0685160     1   B #> 23   53.3559798     1   B #> 24   69.5240787     0   B #> 25   38.8236166     1   B #> 26  204.6022754     0   B #> 27  184.5103590     0   B #> 28   83.5830935     0   B #> 29  114.7067767     1   B #> 30   31.4008382     1   B #> 31   65.7530859     0   B #> 32  163.6438166     1   B #> 33  103.4746398     0   B #> 34  115.6529216     0   B #> 35  119.8663712     1   B #> 36   39.7824824     0   B #> 37   26.2260987     1   B #> 38  168.2133266     1   B #> 39   77.4276997     0   B #> 40  113.1300571     0   B #> 41  341.8226476     0   B #> 42    4.6203917     1   B #> 43   76.1546928     1   B #> 44   31.3239259     1   B #> 45  139.8137748     1   B #> 46   26.2748075     1   B #> 47   78.8443008     0   B #> 48  150.1996343     0   B #> 49    7.5020884     1   B #> 50   27.0121949     1   B #> 51  157.8603589     1   B #> 52  196.4246807     0   B #> 53   21.6935437     0   B #> 54  140.2541753     1   B #> 55   12.1075238     0   B #> 56  107.3694875     1   B #> 57   37.8779261     1   B #> 58  121.0960158     0   B #> 59   20.3595135     1   B #> 60   41.0376828     0   B #> 61   27.4099098     1   B #> 62   14.2204744     1   B #> 63   49.0816396     1   B #> 64   91.8865159     1   B #> 65   20.8240812     0   B #> 66   19.7480647     1   B #> 67   13.4869944     0   B #> 68  297.0789867     1   B #> 69   35.3155408     1   B #> 70   61.3301293     1   B #> 71  236.3826316     0   B #> 72   37.6872463     1   B #> 73   12.4915818     0   B #> 74  117.1516860     1   B #> 75   74.0347874     1   B #> 76   31.8360820     1   B #> 77    6.1533491     0   B #> 78   58.6084245     1   B #> 79    3.7608198     0   B #> 80   32.1776180     1   B #> 81  123.7513692     1   B #> 82  191.6321564     1   B #> 83   49.7949898     0   B #> 84   95.9720009     0   B #> 85   35.3873079     0   B #> 86   93.7520019     1   B #> 87    8.2221920     1   B #> 88   69.4935935     1   B #> 89  115.3801046     0   B #> 90  201.1106058     0   B #> 91  101.8512269     1   B #> 92   16.3430735     0   B #> 93   73.2620925     1   B #> 94   20.9476736     0   B #> 95   13.2074189     1   B #> 96  133.1594500     0   B #> 97    6.7140516     1   B #> 98    2.0758651     0   B #> 99  155.7676131     1   B #> 100 324.6217046     0   B #> 101  57.1030595     1   B #> 102   3.6143976     1   B #> 103  35.3912534     0   B #> 104  48.2754867     1   B #> 105  24.2416986     0   B #> 106   3.2412819     1   B #> 107  77.1943697     0   B #> 108 401.8506175     1   B #> 109  52.2920989     0   B #> 110  14.2519825     0   B #> 111 195.5002523     1   B #> 112   3.1712902     1   B #> 113  58.4270646     1   B #> 114  18.5735263     1   B #> 115  59.6546380     1   B #> 116   6.4806603     1   B #> 117  75.1656036     1   B #> 118   1.7198339     1   B #> 119  84.2445319     0   B #> 120  18.3219669     0   B #> 121  27.3660973     0   B #> 122  23.8855711     0   B #> 123  68.8229804     1   B #> 124  18.9849356     1   B #> 125  37.3202892     1   B #> 126  48.0896160     1   B #> 127  78.4347848     1   B #> 128 128.6077718     0   B #> 129  91.3637782     1   B #> 130  56.9334980     1   B #> 131  13.9279249     1   B #> 132   3.0113133     1   B #> 133  53.6840931     0   B #> 134 109.4392228     1   B #> 135  27.8727602     0   B #> 136  31.3737925     1   B #> 137  36.6028967     0   B #> 138  34.0285658     1   B #> 139  49.9062966     1   B #> 140  47.1405277     0   B #> 141 101.0785894     1   B #> 142  93.6487147     1   B #> 143  20.8436242     1   B #> 144   3.4515079     1   B #> 145  29.8875161     1   B #> 146  63.3996256     0   B #> 147 230.9729773     1   B #> 148 364.3259341     1   B #> 149  12.2323488     1   B #> 150 159.8315468     0   B #> 151 258.5498424     1   B #> 152  24.9889490     0   B #> 153  63.0700888     1   B #> 154 320.4212314     1   B #> 155  90.6121802     1   B #> 156 160.7520774     0   B #> 157  28.8516511     0   B #> 158   8.7919587     0   B #> 159  27.1385849     1   B #> 160  38.2870764     1   B #> 161   7.4210626     0   B #> 162  13.6943879     1   B #> 163 139.8042160     1   B #> 164 170.6566002     0   B #> 165 114.2058151     1   B #> 166 172.4208727     0   B #> 167  39.0678457     1   B #> 168  16.0775600     0   B #> 169  62.4040822     1   B #> 170  95.9224128     1   B #> 171  31.0753223     1   B #> 172   7.3859528     1   B #> 173  13.5637730     0   B #> 174  25.3284722     0   B #> 175 215.0981459     1   B #> 176  13.9126408     1   B #> 177 220.6392416     1   B #> 178  82.5469181     0   B #> 179  10.3249249     0   B #> 180 114.4152727     0   B #> 181  67.4200893     1   B #> 182  16.5377021     1   B #> 183  21.4476172     0   B #> 184 163.3827108     1   B #> 185 114.8396735     0   B #> 186  33.7408843     1   B #> 187  72.9266499     1   B #> 188  85.2637815     1   B #> 189  12.7049720     0   B #> 190  33.5083467     1   B #> 191  64.9965467     1   B #> 192  62.4816013     1   B #> 193  32.6268562     1   B #> 194 230.5868701     1   B #> 195  40.2919970     0   B #> 196  38.4803842     1   B #> 197 116.5770227     1   B #> 198  11.9051673     1   B #> 199  34.7955872     1   B #> 200  48.1972482     1   B #> 201  84.6593786     0   B #> 202 210.0548161     0   B #> 203  32.3201275     0   B #> 204  47.8891543     1   B #> 205  94.4459064     1   B #> 206  27.1427844     1   B #> 207  75.4571413     0   B #> 208  13.0554772     1   B #> 209   1.4030134     1   B #> 210 150.6582394     1   B #> 211  80.4921978     0   B #> 212 102.1683344     1   B #> 213  44.9419892     0   B #> 214 232.5020784     0   B #> 215   2.7979480     0   B #> 216  64.5608166     0   B #> 217  67.5251343     1   B #> 218 129.1025209     1   B #> 219 500.0000000     0   B #> 220  19.5837178     0   B #> 221  70.1227875     0   B #> 222  22.5998941     1   B #> 223  83.5853505     1   B #> 224   8.6680839     1   B #> 225 129.7187580     1   B #> 226  19.3144386     0   B #> 227 333.8879947     1   B #> 228  51.2082623     1   B #> 229  46.6101451     0   B #> 230  73.6926698     1   B #> 231  34.3307012     0   B #> 232  76.4192775     1   B #> 233   2.9805455     0   B #> 234 127.4336784     0   B #> 235   3.4211613     0   B #> 236  54.9236065     1   B #> 237 294.1386975     1   B #> 238  15.2915139     0   B #> 239  56.8481500     1   B #> 240  55.3458512     0   B #> 241   7.7622930     1   B #> 242  93.8619589     0   B #> 243 177.5091814     0   B #> 244  70.2628625     0   B #> 245  41.2949253     1   B #> 246 129.7250459     1   B #> 247   5.5211413     0   B #> 248  38.4697339     1   B #> 249  38.4278985     0   B #> 250  37.9046463     1   B #> 251  29.7580207     0   B #> 252  37.3557166     0   B #> 253   2.3393342     1   B #> 254 116.2388631     0   B #> 255  37.1140694     1   B #> 256  93.2119835     1   B #> 257  16.7345687     0   B #> 258  28.7863941     0   B #> 259  17.0540489     1   B #> 260  60.2527239     0   B #> 261  56.7171853     0   B #> 262  22.2428646     1   B #> 263  75.5904943     0   B #> 264  32.8579123     1   B #> 265 105.5323827     0   B #> 266 124.2150196     1   B #> 267  90.5420118     1   B #> 268  55.0154610     1   B #> 269 215.5597419     1   B #> 270   5.5773040     1   B #> 271 144.2521772     0   B #> 272  69.0486551     1   B #> 273 212.7464896     1   B #> 274 128.9828791     0   B #> 275  26.6127303     1   B #> 276  12.2191633     1   B #> 277  18.6707015     0   B #> 278 175.4929594     0   B #> 279  40.8238759     1   B #> 280  69.7451992     1   B #> 281  47.8510476     0   B #> 282  18.9679808     0   B #> 283  61.2068279     1   B #> 284  44.8460398     1   B #> 285 199.8426419     1   B #> 286  58.7730149     1   B #> 287  64.6149696     1   B #> 288  38.9656553     0   B #> 289  17.7346078     1   B #> 290  64.0457781     1   B #> 291  40.1446851     1   B #> 292  73.5593273     1   B #> 293  68.5812580     0   B #> 294  96.2560394     1   B #> 295  10.5896188     1   B #> 296 111.0420553     0   B #> 297  22.4233793     1   B #> 298   9.0396962     1   B #> 299 143.5751793     0   B #> 300  15.1259399     0   B #> 301  31.9007805     1   C #> 302 224.9210305     1   C #> 303  40.8121892     1   C #> 304   2.8986329     1   C #> 305 122.7150417     1   C #> 306 117.2979372     1   C #> 307 139.8538186     1   C #> 308 198.6359612     1   C #> 309  45.7984108     1   C #> 310  51.5045070     1   C #> 311 284.6505775     1   C #> 312   8.2054956     1   C #> 313  37.3399724     1   C #> 314  78.4380157     1   C #> 315  67.6653058     1   C #> 316  35.8826731     1   C #> 317 206.4266946     1   C #> 318   4.4685532     1   C #> 319  21.7769869     1   C #> 320   9.2258614     1   C #> 321  49.3131530     1   C #> 322  53.7385301     1   C #> 323  80.6121057     1   C #> 324 126.1661283     1   C #> 325   7.9635105     1   C #> 326  16.2166981     1   C #> 327 187.8325651     1   C #> 328  16.7471612     1   C #> 329   8.6766179     1   C #> 330   6.1161740     1   C #> 331 276.2456938     1   C #> 332 361.8332113     1   C #> 333  58.4359040     1   C #> 334  85.4101501     1   C #> 335   9.5951805     1   C #> 336   2.8626645     1   C #> 337   3.8211774     1   C #> 338  14.2525023     1   C #> 339  20.8885622     1   C #> 340 244.8500372     1   C #> 341  94.6601919     1   C #> 342  42.6863882     1   C #> 343  53.2908582     1   C #> 344  34.8085622     1   C #> 345  52.9124016     1   C #> 346 169.4117331     1   C #> 347 107.1202631     1   C #> 348 211.5530765     1   C #> 349  47.4429379     1   C #> 350  43.8325267     1   C #> 351  40.1064231     1   C #> 352  32.2150048     1   C #> 353  56.6569594     1   C #> 354  22.1794129     1   C #> 355  76.2141403     1   C #> 356  83.9255240     1   C #> 357  18.2860990     1   C #> 358  39.1744099     1   C #> 359   6.2007373     1   C #> 360 266.3275331     1   C #> 361  63.3110955     1   C #> 362  17.6230889     1   C #> 363 115.5534530     1   C #> 364  82.5114275     1   C #> 365 186.3398042     1   C #> 366  30.1885913     1   C #> 367  30.0502707     1   C #> 368  48.9121396     1   C #> 369 141.4487129     1   C #> 370 171.7598873     1   C #> 371  20.7614482     1   C #> 372  76.8425139     1   C #> 373  43.2498692     1   C #> 374  68.4211613     1   C #> 375 127.3104848     1   C #> 376  32.4396500     1   C #> 377  71.0596804     1   C #> 378 139.1435945     1   C #> 379 211.5398906     1   C #> 380 376.1052610     1   C #> 381  44.7877585     1   C #> 382  35.6373159     1   C #> 383 195.7309782     1   C #> 384 359.0609929     1   C #> 385  87.9252899     1   C #> 386  63.9314490     1   C #> 387  20.1651679     1   C #> 388 328.2676133     1   C #> 389 122.3091982     1   C #> 390  76.2564232     1   C #> 391  60.3352419     1   C #> 392  46.7123059     1   C #> 393 170.2945732     1   C #> 394  53.9880957     1   C #> 395 139.9918337     1   C #> 396   4.2157213     1   C #> 397  59.6334728     1   C #> 398  62.7271303     1   C #> 399  54.7736547     1   C #> 400  25.7911074     1   C #> 401  70.5377511     1   C #> 402  90.4953554     1   C #> 403  68.7783819     1   C #> 404  33.7365728     1   C #> 405 254.1279077     1   C #> 406 236.5857254     1   C #> 407 410.6348769     1   C #> 408  23.3445397     1   C #> 409  63.9301624     1   C #> 410  49.2088476     1   C #> 411 311.1617654     1   C #> 412  66.2181329     1   C #> 413 170.5941866     1   C #> 414  93.5285471     1   C #> 415   9.4629687     1   C #> 416 165.3656360     1   C #> 417 168.8080453     1   C #> 418   5.1997817     1   C #> 419  56.5679692     1   C #> 420   6.2655921     1   C #> 421  46.0280852     1   C #> 422  20.3899054     1   C #> 423  52.2663301     1   C #> 424 395.3935200     1   C #> 425  28.0691047     1   C #> 426 130.3141560     1   C #> 427 137.2698861     1   C #> 428  38.2862049     1   C #> 429  47.6901231     1   C #> 430 138.0820810     1   C #> 431  32.1799785     1   C #> 432 112.3051926     1   C #> 433  19.9185602     1   C #> 434  76.2920091     1   C #> 435  56.6825019     1   C #> 436 179.0610637     1   C #> 437  69.1740697     1   C #> 438   2.2505426     1   C #> 439  66.6049264     1   C #> 440  26.9593252     1   C #> 441 124.2339656     1   C #> 442  49.5979148     1   C #> 443   7.1688156     1   C #> 444  86.9592721     1   C #> 445  26.6045310     1   C #> 446  43.3911009     1   C #> 447 128.9630328     1   C #> 448  80.7758703     1   C #> 449 152.6211335     1   C #> 450  46.9792384     1   C #> 451  27.2728535     1   C #> 452  53.4348720     1   C #> 453  68.2000078     1   C #> 454   2.8706938     1   C #> 455  89.2778020     1   C #> 456  73.7624779     1   C #> 457  66.9368711     1   C #> 458 137.9830289     1   C #> 459  15.6148074     1   C #> 460   2.4549901     1   C #> 461   0.4547303     1   C #> 462  61.2325835     1   C #> 463 103.7235743     1   C #> 464  37.5361795     1   C #> 465  55.5583022     1   C #> 466  11.9588032     1   C #> 467 349.9271334     1   C #> 468 197.7737516     1   C #> 469   0.4679593     1   C #> 470   6.6126079     1   C #> 471 106.1618294     1   C #> 472 338.4375734     1   C #> 473 207.4153120     1   C #> 474 206.3535025     1   C #> 475  11.8902070     1   C #> 476  83.0679504     1   C #> 477  35.3359441     1   C #> 478  26.3272993     1   C #> 479  56.3209696     1   C #> 480  86.4534772     1   C #> 481  26.0348507     1   C #> 482 129.4389274     1   C #> 483  12.6744570     1   C #> 484 211.0183539     1   C #> 485  64.4749462     1   C #> 486  40.2295100     1   C #> 487  44.0939911     1   C #> 488  57.4278961     1   C #> 489 346.9549975     1   C #> 490  29.7902965     1   C #> 491 219.5845329     1   C #> 492  16.2093773     1   C #> 493   3.5508758     1   C #> 494  14.7713947     1   C #> 495 303.9363812     1   C #> 496  16.5357401     1   C #> 497  50.8742355     1   C #> 498  94.5126584     1   C #> 499 173.5118784     1   C #> 500  17.7684122     1   C #> 501 134.5956768     1   C #> 502   3.7226013     1   C #> 503 101.2550594     1   C #> 504  82.2755429     1   C #> 505  18.2113050     1   C #> 506 102.7671387     1   C #> 507  39.3929382     1   C #> 508 102.5566076     1   C #> 509 238.8303887     1   C #> 510  32.2957137     1   C #> 511  71.5848306     1   C #> 512 217.1808990     1   C #> 513  11.5481860     1   C #> 514  50.9232784     1   C #> 515 241.0593326     1   C #> 516   0.9898505     1   C #> 517 270.9729398     1   C #> 518   0.8868238     1   C #> 519  21.4578476     1   C #> 520   5.4217727     1   C #> 521 122.4817571     1   C #> 522 130.6844099     1   C #> 523  14.5684157     1   C #> 524  15.9398174     1   C #> 525  24.3319339     1   C #> 526  38.1484547     1   C #> 527   8.6328602     1   C #> 528 216.8900398     1   C #> 529  28.7670428     1   C #> 530  57.8968761     1   C #> 531 189.0947674     1   C #> 532 195.8796317     1   C #> 533   6.7554534     1   C #> 534  55.4418422     1   C #> 535  14.9219480     1   C #> 536  56.7754443     1   C #> 537  65.9951328     1   C #> 538  59.1533520     1   C #> 539  54.5515559     1   C #> 540   0.7455647     1   C #> 541   3.5424647     1   C #> 542 213.1069769     1   C #> 543  15.2995420     1   C #> 544  62.3742666     1   C #> 545 188.2253712     1   C #> 546 109.8569379     1   C #> 547 105.3056303     1   C #> 548 181.7751578     1   C #> 549 109.8185480     1   C #> 550  58.9768111     1   C #> 551 156.6727094     1   C #> 552 123.0597689     1   C #> 553  17.1803281     1   C #> 554  12.7273790     1   C #> 555  27.2453182     1   C #> 556  19.7146427     1   C #> 557  36.6349982     1   C #> 558 272.3752120     1   C #> 559  36.3879036     1   C #> 560 146.0949964     1   C #> 561  61.0101546     1   C #> 562 360.1304361     1   C #> 563 125.1880061     1   C #> 564 181.6820019     1   C #> 565 432.3495904     1   C #> 566  59.3855396     1   C #> 567   0.9624553     1   C #> 568 149.3668701     1   C #> 569  55.9411352     1   C #> 570  73.9574770     1   C #> 571  52.3685641     1   C #> 572 280.9762834     1   C #> 573   9.9605350     1   C #> 574 168.2686229     1   C #> 575 109.0800311     1   C #> 576   1.7781866     1   C #> 577 192.0572072     1   C #> 578 121.6663948     1   C #> 579  40.9598000     1   C #> 580   4.7165329     1   C #> 581 152.1987498     1   C #> 582   2.4999375     1   C #> 583  62.8220411     1   C #> 584  33.3294665     1   C #> 585  20.3195787     1   C #> 586  59.9037111     1   C #> 587 276.1406159     1   C #> 588   1.8819531     1   C #> 589  37.0348936     1   C #> 590  23.6847101     1   C #> 591 122.1550133     1   C #> 592  69.6353349     1   C #> 593  10.1666166     1   C #> 594 308.0106026     1   C #> 595  92.5965371     1   C #> 596 207.9073549     1   C #> 597 306.9951522     1   C #> 598  29.8032770     1   C #> 599  63.5375426     1   C #> 600  60.9214087     1   C #> 601   1.8974623     1   C #> 602  38.1757393     1   C #> 603  94.0161631     1   C #> 604  68.4254251     1   C #> 605  85.0044681     1   C #> 606  28.8755003     1   C #> 607  45.4793288     1   C #> 608   3.8261500     1   C #> 609  30.1251444     1   C #> 610 179.4245793     1   C #> 611  33.8091982     1   C #> 612  98.3950780     1   C #> 613   9.8651749     1   C #> 614   6.1270467     1   C #> 615 203.2524779     1   C #> 616  80.7678195     1   C #> 617  54.2690465     1   C #> 618   5.2908184     1   C #> 619  17.6934652     1   C #> 620 105.7681953     1   C #> 621  37.9967270     1   C #> 622 128.7672423     1   C #> 623  95.2012045     1   C #> 624  75.5118328     1   C #> 625  28.2900272     1   C #> 626  29.0599836     1   C #> 627 385.0733918     1   C #> 628  75.7357239     1   C #> 629 180.0774981     1   C #> 630   0.2504356     1   C #> 631   7.4870408     1   C #> 632 109.4101567     1   C #> 633  54.4805610     1   C #> 634 150.4004485     1   C #> 635  60.2235611     1   C #> 636  79.4531928     1   C #> 637 208.6755296     1   C #> 638  41.8376173     1   C #> 639  13.2705437     1   C #> 640  42.3934947     1   C #> 641  25.5786448     1   C #> 642  24.1257563     1   C #> 643  25.6245013     1   C #> 644  42.3522825     1   C #> 645  30.3316336     1   C #> 646  64.2448360     1   C #> 647  65.2345577     1   C #> 648   2.9259080     1   C #> 649   0.7544762     1   C #> 650   9.8111783     1   C #> 651  54.5728111     1   C #> 652  14.8313343     1   C #> 653  92.4349812     1   C #> 654 206.9897115     1   C #> 655   4.3363978     1   C #> 656 126.3544321     1   C #> 657  61.1648358     1   C #> 658   7.2121233     1   C #> 659  20.1326000     1   C #> 660  97.3169365     1   C #> 661  12.3515482     1   C #> 662 125.5980355     1   C #> 663  33.0257509     1   C #> 664  52.4541262     1   C #> 665  69.8797378     1   C #> 666  20.5634573     1   C #> 667 202.0092538     1   C #> 668   0.7634996     1   C #> 669 176.1329749     1   C #> 670  25.8601898     1   C #> 671 119.4433218     1   C #> 672  50.4333857     1   C #> 673  33.3157194     1   C #> 674  36.2734314     1   C #> 675  17.3770767     1   C #> 676 333.2811826     1   C #> 677 133.1432645     1   C #> 678   5.9647760     1   C #> 679  42.6350708     1   C #> 680 260.8186123     1   C #> 681  15.7038074     1   C #> 682  61.4575576     1   C #> 683  29.6649397     1   C #> 684  88.3704071     1   C #> 685  37.8367104     1   C #> 686  44.8863824     1   C #> 687 131.1053585     1   C #> 688   4.2570066     1   C #> 689  18.2785781     1   C #> 690  46.5918226     1   C #> 691  17.9491164     1   C #> 692  22.3337852     1   C #> 693  72.4316778     1   C #> 694  51.7076510     1   C #> 695 141.0888687     1   C #> 696  26.3551823     1   C #> 697  73.9750546     1   C #> 698 224.8529753     1   C #> 699 331.4151551     1   C #> 700  33.9325202     1   C #> 701  37.8032221     1   C #> 702  10.0572560     1   C #> 703  24.1215638     1   C #> 704  26.8714586     1   C #> 705  45.3035811     1   C #> 706  28.1369080     1   C #> 707   5.2267033     1   C #> 708  99.1843533     1   C #> 709   0.9260236     1   C #> 710 141.1788408     1   C #> 711 351.1399865     1   C #> 712  22.1090153     1   C #> 713 159.1137213     1   C #> 714  38.3125332     1   C #> 715 102.4666222     1   C #> 716  22.5130915     1   C #> 717  67.3724711     1   C #> 718   5.2977519     1   C #> 719 338.2797970     1   C #> 720 324.2844532     1   C #> 721  35.1382473     1   C #> 722  13.1855325     1   C #> 723  82.1709191     1   C #> 724  35.3322755     1   C #> 725  59.9593228     1   C #> 726 132.8322791     1   C #> 727   2.3335002     1   C #> 728  34.7189146     1   C #> 729  55.7721040     1   C #> 730  72.5377629     1   C #> 731 126.0833015     1   C #> 732  82.1371958     1   C #> 733  79.8656971     1   C #> 734  91.0498106     1   C #> 735 166.6115962     1   C #> 736 176.8942873     1   C #> 737 192.1270684     1   C #> 738  39.2302592     1   C #> 739   6.3414907     1   C #> 740   3.8759611     1   C #> 741  29.9652297     1   C #> 742   6.1536886     1   C #> 743  48.7141680     1   C #> 744   0.0990051     1   C #> 745  44.6563684     1   C #> 746  58.0575307     1   C #> 747  23.2741787     1   C #> 748 139.0004481     1   C #> 749  13.9091483     1   C #> 750  40.8809036     1   C #> 751 130.0792075     1   C #> 752  35.4366759     1   C #> 753  65.6279325     1   C #> 754  14.8027962     1   C #> 755 115.5402014     1   C #> 756 147.6945953     1   C #> 757  21.0786068     1   C #> 758  12.7715724     1   C #> 759  35.7647948     1   C #> 760  16.2384083     1   C #> 761 142.5683763     1   C #> 762  48.2883192     1   C #> 763  14.4747400     1   C #> 764  70.7480995     1   C #> 765 142.5645692     1   C #> 766  20.0736197     1   C #> 767  38.4760124     1   C #> 768  72.6307495     1   C #> 769 500.0000000     0   C #> 770  58.2882059     1   C #> 771  34.5957184     1   C #> 772  24.5623167     1   C #> 773  86.6690970     1   C #> 774   1.5079031     1   C #> 775  31.2528024     1   C #> 776  32.2891538     1   C #> 777   2.0602410     1   C #> 778  45.1162448     1   C #> 779  37.6824055     1   C #> 780   4.1874008     1   C #> 781 137.1278015     1   C #> 782  49.8113560     1   C #> 783 500.0000000     0   C #> 784  46.9213552     1   C #> 785  90.4030377     1   C #> 786 400.3366689     1   C #> 787 124.6043711     1   C #> 788 104.7838825     1   C #> 789 159.0973847     1   C #> 790  78.6201401     1   C #> 791  13.5408137     1   C #> 792  19.2799127     1   C #> 793  49.8818012     1   C #> 794  52.2137377     1   C #> 795 274.4921166     1   C #> 796  16.1620879     1   C #> 797  85.2940604     1   C #> 798  36.9315347     1   C #> 799 217.8616656     1   C #> 800 139.2077015     1   C  #### prepare data target_pop <- process_agd(agd) adsl <- dummize_ipd(adsl_twt, dummize_cols = c(\"SEX\"), dummize_ref_level = c(\"Female\")) use_adsl <- center_ipd(ipd = adsl, agd = target_pop)  #### derive weights match_res <- estimate_weights(   data = use_adsl,   centered_colnames = grep(\"_CENTERED$\", names(use_adsl)),   start_val = 0,   method = \"BFGS\" )  # plot by trial kmplot(   weights_object = match_res,   tte_ipd = adtte_twt,   trt_var_ipd = \"ARM\",   tte_pseudo_ipd = pseudo_ipd_twt,   trt_var_agd = \"ARM\",   endpoint_name = \"Overall Survival\",   trt_ipd = \"A\",   trt_agd = \"B\",   trt_common = \"C\",   km_conf_type = \"log-log\",   km_layout = \"by_trial\",   time_scale = \"month\",   time_grid = seq(0, 20, by = 2),   use_colors = NULL,   use_line_types = NULL,   use_pch_cex = 0.65,   use_pch_alpha = 100 )     # plot by arm kmplot(   weights_object = match_res,   tte_ipd = adtte_twt,   trt_var_ipd = \"ARM\",   tte_pseudo_ipd = pseudo_ipd_twt,   trt_var_agd = \"ARM\",   endpoint_name = \"Overall Survival\",   trt_ipd = \"A\",   trt_agd = \"B\",   trt_common = \"C\",   km_conf_type = \"log-log\",   km_layout = \"by_arm\",   time_scale = \"month\",   time_grid = seq(0, 20, by = 2),   use_colors = NULL,   use_line_types = NULL,   use_pch_cex = 0.65,   use_pch_alpha = 100 )    # plot all kmplot(   weights_object = match_res,   tte_ipd = adtte_twt,   trt_var_ipd = \"ARM\",   tte_pseudo_ipd = pseudo_ipd_twt,   trt_var_agd = \"ARM\",   endpoint_name = \"Overall Survival\",   trt_ipd = \"A\",   trt_agd = \"B\",   trt_common = \"C\",   km_conf_type = \"log-log\",   km_layout = \"all\",   time_scale = \"month\",   time_grid = seq(0, 20, by = 2),   use_colors = NULL,   use_line_types = NULL,   use_pch_cex = 0.65,   use_pch_alpha = 100 )     # unanchored example using maicplus_kmplot  ### IPD # Read in relevant ADaM data and rename variables of interest adsl <- read.csv(system.file(\"extdata\", \"adsl.csv\",   package = \"maicplus\",   mustWork = TRUE )) adtte <- read.csv(system.file(\"extdata\", \"adtte.csv\",   package = \"maicplus\",   mustWork = TRUE )) adtte$TIME <- adtte$AVAL adtte$EVENT <- adtte$EVNT  ### AgD # Baseline aggregate data for the comparator population target_pop <- read.csv(system.file(\"extdata\", \"aggregate_data_example_1.csv\",   package = \"maicplus\", mustWork = TRUE )) # for time-to-event endpoints, pseudo IPD from digitalized KM pseudo_ipd <- read.csv(system.file(\"extdata\", \"psuedo_IPD.csv\",   package = \"maicplus\",   mustWork = TRUE )) pseudo_ipd$ARM <- \"B\"  #### prepare data target_pop <- process_agd(target_pop) adsl <- dummize_ipd(adsl, dummize_cols = c(\"SEX\"), dummize_ref_level = c(\"Female\")) use_adsl <- center_ipd(ipd = adsl, agd = target_pop)  #### derive weights match_res <- estimate_weights(   data = use_adsl,   centered_colnames = grep(\"_CENTERED$\", names(use_adsl)),   start_val = 0,   method = \"BFGS\" )  # plot kmplot(   weights_object = match_res,   tte_ipd = adtte,   trt_var_ipd = \"ARM\",   tte_pseudo_ipd = pseudo_ipd,   trt_var_agd = \"ARM\",   endpoint_name = \"Overall Survival\",   trt_ipd = \"A\",   trt_agd = \"B\",   trt_common = NULL,   km_conf_type = \"log-log\",   time_scale = \"month\",   time_grid = seq(0, 20, by = 2),   use_colors = NULL,   use_line_types = NULL,   use_pch_cex = 0.65,   use_pch_alpha = 100 )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/kmplot2.html","id":null,"dir":"Reference","previous_headings":"","what":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","title":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","text":"wrapper function basic_kmplot2. argument setting similar maic_anchored maic_unanchored, used two functions.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/kmplot2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","text":"","code":"kmplot2(   weights_object,   tte_ipd,   tte_pseudo_ipd,   trt_ipd,   trt_agd,   trt_common = NULL,   trt_var_ipd = \"ARM\",   trt_var_agd = \"ARM\",   km_conf_type = \"log-log\",   km_layout = c(\"all\", \"by_trial\", \"by_arm\"),   time_scale,   ... )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/kmplot2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","text":"weights_object object returned estimate_weight tte_ipd data frame individual patient data (IPD) internal trial, contain least \"USUBJID\", \"EVENT\", \"TIME\" columns column indicating treatment assignment tte_pseudo_ipd data frame pseudo IPD digitized KM curves external trial (time--event endpoint), contain least \"EVENT\", \"TIME\" trt_ipd string, name interested investigation arm internal trial dat_igd (real IPD) trt_agd string, name interested investigation arm external trial dat_pseudo (pseudo IPD) trt_common string, name common comparator internal external trial, default NULL, indicating unanchored case trt_var_ipd string, column name tte_ipd contains treatment assignment trt_var_agd string, column name tte_pseudo_ipd contains treatment assignment km_conf_type string, pass conf.type survfit km_layout string, applicable unanchored case (trt_common = NULL), indicated desired layout output KM curve. time_scale string, time unit median survival time, taking value 'years', 'months', weeks' 'days' ... arguments basic_kmplot2","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/kmplot2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","text":"unanchored case, KM plot risk set table. anchored case, depending km_layout, \"by_trial\", 2 1 plot, first KM curves (incl. weighted) IPD trial, KM curves AgD trial, risk set table. \"by_arm\", 2 1 plot, first KM curves trt_agd  trt_ipd (without weights), KM curves trt_common AgD trial IPD trial (without weights). Risk set table appended. \"\", 2 2 plot, plots \"by_trial\" \"by_arm\" without risk set table appended.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/kmplot2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","text":"","code":"# anchored example using kmplot  ### IPD # Read in relevant ADaM data and rename variables of interest adsl <- read.csv(system.file(\"extdata\", \"adsl.csv\",   package = \"maicplus\",   mustWork = TRUE )) adtte <- read.csv(system.file(\"extdata\", \"adtte.csv\",   package = \"maicplus\",   mustWork = TRUE )) adtte$TIME <- adtte$AVAL adtte$EVENT <- adtte$EVNT adtte2 <- adtte adtte2$ARM <- \"C\" adtte2$TIME <- adtte2$TIME + 7 adtte <- rbind(adtte, adtte2)  ### AgD # Baseline aggregate data for the comparator population target_pop <- read.csv(system.file(\"extdata\", \"aggregate_data_example_1.csv\",   package = \"maicplus\", mustWork = TRUE )) # for time-to-event endpoints, pseudo IPD from digitalized KM pseudo_ipd <- read.csv(system.file(\"extdata\", \"psuedo_IPD.csv\",   package = \"maicplus\",   mustWork = TRUE )) pseudo_ipd$ARM <- \"B\" pseudo_ipd2 <- pseudo_ipd pseudo_ipd2$ARM <- \"C\" pseudo_ipd2$Time <- pseudo_ipd2$Time + 5 pseudo_ipd <- rbind(pseudo_ipd, pseudo_ipd2)  #### prepare data target_pop <- process_agd(target_pop) adsl <- dummize_ipd(adsl, dummize_cols = c(\"SEX\"), dummize_ref_level = c(\"Female\")) use_adsl <- center_ipd(ipd = adsl, agd = target_pop)  #### derive weights match_res <- estimate_weights(   data = use_adsl,   centered_colnames = grep(\"_CENTERED$\", names(use_adsl)),   start_val = 0,   method = \"BFGS\" )  # plot by trial kmplot2(   weights_object = match_res,   tte_ipd = adtte,   tte_pseudo_ipd = pseudo_ipd,   trt_ipd = \"A\",   trt_agd = \"B\",   trt_common = \"C\",   trt_var_ipd = \"ARM\",   trt_var_agd = \"ARM\",   endpoint_name = \"Overall Survival\",   km_conf_type = \"log-log\",   km_layout = \"by_trial\",   time_scale = \"month\",   break_x_by = 2 ) #> Warning: There was 1 warning in `mutate()`. #> ℹ In argument: `survtable = purrr::map2(...)`. #> Caused by warning: #> ! `select_()` was deprecated in dplyr 0.7.0. #> ℹ Please use `select()` instead. #> ℹ The deprecated feature was likely used in the survminer package. #>   Please report the issue at <https://github.com/kassambara/survminer/issues>.   # plot by arm kmplot2(   weights_object = match_res,   tte_ipd = adtte,   tte_pseudo_ipd = pseudo_ipd,   trt_ipd = \"A\",   trt_agd = \"B\",   trt_common = \"C\",   trt_var_ipd = \"ARM\",   trt_var_agd = \"ARM\",   endpoint_name = \"Overall Survival\",   km_conf_type = \"log-log\",   km_layout = \"by_arm\",   time_scale = \"month\",   break_x_by = 2 )   # plot all kmplot2(   weights_object = match_res,   tte_ipd = adtte,   tte_pseudo_ipd = pseudo_ipd,   trt_ipd = \"A\",   trt_agd = \"B\",   trt_common = \"C\",   trt_var_ipd = \"ARM\",   trt_var_agd = \"ARM\",   endpoint_name = \"Overall Survival\",   km_conf_type = \"log-log\",   km_layout = \"all\",   time_scale = \"month\",   break_x_by = 2,   xlim = c(0, 20) )  # unanchored example using maicplus_kmplot  ### IPD # Read in relevant ADaM data and rename variables of interest adsl <- read.csv(system.file(\"extdata\", \"adsl.csv\",   package = \"maicplus\",   mustWork = TRUE )) adtte <- read.csv(system.file(\"extdata\", \"adtte.csv\",   package = \"maicplus\",   mustWork = TRUE )) adtte$TIME <- adtte$AVAL adtte$EVENT <- adtte$EVNT  ### AgD # Baseline aggregate data for the comparator population target_pop <- read.csv(system.file(\"extdata\", \"aggregate_data_example_1.csv\",   package = \"maicplus\", mustWork = TRUE )) # for time-to-event endpoints, pseudo IPD from digitalized KM pseudo_ipd <- read.csv(system.file(\"extdata\", \"psuedo_IPD.csv\",   package = \"maicplus\",   mustWork = TRUE )) pseudo_ipd$ARM <- \"B\"  #### prepare data target_pop <- process_agd(target_pop) adsl <- dummize_ipd(adsl, dummize_cols = c(\"SEX\"), dummize_ref_level = c(\"Female\")) use_adsl <- center_ipd(ipd = adsl, agd = target_pop)  #### derive weights match_res <- estimate_weights(   data = use_adsl,   centered_colnames = grep(\"_CENTERED$\", names(use_adsl)),   start_val = 0,   method = \"BFGS\" )   kmplot2(   weights_object = match_res,   tte_ipd = adtte,   tte_pseudo_ipd = pseudo_ipd,   trt_ipd = \"A\",   trt_agd = \"B\",   trt_common = NULL,   trt_var_ipd = \"ARM\",   trt_var_agd = \"ARM\",   endpoint_name = \"Overall Survival\",   km_conf_type = \"log-log\",   time_scale = \"month\",   break_x_by = 2,   xlim = c(0, 20) )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maic_anchored.html","id":null,"dir":"Reference","previous_headings":"","what":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","title":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","text":"wrapper function provide adjusted effect estimates relevant statistics anchored case (.e. common comparator arm internal external trial).","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maic_anchored.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","text":"","code":"maic_anchored(   weights_object,   ipd,   pseudo_ipd,   trt_ipd,   trt_agd,   trt_common,   trt_var_ipd = \"ARM\",   trt_var_agd = \"ARM\",   endpoint_type = \"tte\",   endpoint_name = \"Time to Event Endpoint\",   eff_measure = c(\"HR\", \"OR\", \"RR\", \"RD\", \"Diff\"),   time_scale = \"months\",   km_conf_type = \"log-log\",   boot_ci_type = c(\"norm\", \"basic\", \"stud\", \"perc\", \"bca\") )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maic_anchored.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","text":"weights_object object returned estimate_weight ipd data frame meet format requirements 'Details', individual patient data (IPD) internal trial pseudo_ipd data frame, pseudo IPD digitized KM curve external trial (time--event endpoint) contingency table (binary endpoint) trt_ipd string, name interested investigation arm internal trial ipd (internal IPD) trt_agd string, name interested investigation arm external trial pseudo_ipd (pseudo IPD) trt_common string, name common comparator internal external trial trt_var_ipd string, column name ipd contains treatment assignment trt_var_agd string, column name ipd contains treatment assignment endpoint_type string, one following \"binary\", \"tte\" (time event) endpoint_name string, name time event endpoint, show last line title eff_measure string, \"RD\" (risk difference), \"\" (odds ratio), \"RR\" (relative risk) binary endpoint; \"HR\" time--event endpoint. default NULL, \"\" used binary case, otherwise \"HR\" used. time_scale string, time unit median survival time, taking value 'years', 'months', 'weeks' 'days'. NOTE: assumed values TIME column ipd pseudo_ipd unit days km_conf_type string, pass conf.type survfit boot_ci_type string, one c(\"norm\",\"basic\", \"stud\", \"perc\", \"bca\") select type bootstrap confidence interval. See boot::boot.ci details.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maic_anchored.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","text":"list, contains 'descriptive' 'inferential'","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maic_anchored.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","text":"time--event analysis, required input ipd pseudo_ipd following columns. function sensitive upper lower case letters column names. USUBJID - character, unique subject ID ARM - character factor, treatment indicator, column name 'ARM'. User specify trt_var_ipd trt_var_agd EVENT - numeric, 1 censored/death, 0 otherwise TIME - numeric column, observation time EVENT; unit days","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maic_unanchored.html","id":null,"dir":"Reference","previous_headings":"","what":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","title":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","text":"wrapper function provide adjusted effect estimates relevant statistics unanchored case (.e. common comparator arm internal external trial).","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maic_unanchored.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","text":"","code":"maic_unanchored(   weights_object,   ipd,   pseudo_ipd,   trt_ipd,   trt_agd,   trt_var_ipd = \"ARM\",   trt_var_agd = \"ARM\",   endpoint_type = \"tte\",   endpoint_name = \"Time to Event Endpoint\",   eff_measure = c(\"HR\", \"OR\", \"RR\", \"RD\"),   boot_ci_type = c(\"norm\", \"basic\", \"stud\", \"perc\", \"bca\"),   time_scale = \"months\",   km_conf_type = \"log-log\",   binary_robust_cov_type = \"HC3\" )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maic_unanchored.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","text":"weights_object object returned estimate_weight ipd data frame meet format requirements 'Details', individual patient data (IPD) internal trial pseudo_ipd data frame, pseudo IPD digitized KM curve external trial (time--event endpoint) contingency table (binary endpoint) trt_ipd string, name interested investigation arm internal trial dat_igd (real IPD) trt_agd string, name interested investigation arm external trial pseudo_ipd (pseudo IPD) trt_var_ipd string, column name ipd contains treatment assignment trt_var_agd string, column name ipd contains treatment assignment endpoint_type string, one following \"binary\", \"tte\" (time event) endpoint_name string, name time event endpoint, show last line title eff_measure string, \"RD\" (risk difference), \"\" (odds ratio), \"RR\" (relative risk) binary endpoint; \"HR\" time--event endpoint. default NULL, \"\" used binary case, otherwise \"HR\" used. boot_ci_type string, one c(\"norm\",\"basic\", \"stud\", \"perc\", \"bca\") select type bootstrap confidence interval. See boot::boot.ci details. time_scale string, time unit median survival time, taking value 'years', 'months', 'weeks' 'days'. NOTE: assumed values TIME column ipd pseudo_ipd unit days km_conf_type string, pass conf.type survfit binary_robust_cov_type string pass argument type sandwich::vcovHC, see possible options documentation function. Default \"HC3\"","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maic_unanchored.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","text":"list, contains 'descriptive' 'inferential'","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maic_unanchored.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","text":"time--event analysis, required input ipd pseudo_ipd following columns. function sensitive upper lower case letters column names. USUBJID - character, unique subject ID ARM - character factor, treatment indicator, column name 'ARM'. User specify trt_var_ipd trt_var_agd EVENT - numeric, 1 censored/death, 0 otherwise TIME - numeric column, observation time EVENT; unit days","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maicplus-package.html","id":null,"dir":"Reference","previous_headings":"","what":"maicplus: Matching Adjusted Indirect Comparison — maicplus-package","title":"maicplus: Matching Adjusted Indirect Comparison — maicplus-package","text":"maicplus package facilitates performing matching adjusted indirect comparison (MAIC) analysis endpoint interest either time--event (e.g. overall survival) binary (e.g. objective tumor response).","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/maicplus-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"maicplus: Matching Adjusted Indirect Comparison — maicplus-package","text":"Maintainer: hta-pharma hta-pharma@example.com","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/medSurv_makeup.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","title":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","text":"Extract display median survival time confidence interval","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/medSurv_makeup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","text":"","code":"medSurv_makeup(km_fit, legend = \"before matching\", time_scale)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/medSurv_makeup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","text":"km_fit returned object survival::survfit legend character string, name used 'type' column returned data frame time_scale character string, 'years', 'months', 'weeks' 'days', time unit median survival time","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/medSurv_makeup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","text":"data frame index column 'type', median survival time confidence interval","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/medSurv_makeup.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","text":"","code":"library(survival) load(system.file(\"extdata\", \"combined_data_tte.rda\", package = \"maicplus\", mustWork = TRUE)) kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data_tte, conf.type = \"log-log\") kmobj_adj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data_tte,   weights = combined_data_tte$weights, conf.type = \"log-log\" )  # Derive median survival time medSurv <- medSurv_makeup(kmobj, legend = \"before matching\", time_scale = \"day\") medSurv_adj <- medSurv_makeup(kmobj_adj, legend = \"after matching\", time_scale = \"day\") medSurv_out <- rbind(medSurv, medSurv_adj) medSurv_out #>   treatment            type records    n.max  n.start    events    rmean #> 1     ARM=A before matching     500 500.0000 500.0000 190.00000 265.1012 #> 2     ARM=B before matching     300 300.0000 300.0000 178.00000 130.9893 #> 3     ARM=A  after matching     500 199.8422 199.8422  66.84953 307.7223 #> 4     ARM=B  after matching     300 300.0000 300.0000 178.00000 130.9893 #>   se(rmean)    median   0.95LCL  0.95UCL #> 1  10.80981 230.94839 191.10767 313.1574 #> 2  10.24910  83.58535  68.82298 101.0786 #> 3  16.71338 362.20670 237.05932 452.7209 #> 4  10.24910  83.58535  68.82298 101.0786"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnosis plot of proportional hazard assumption for anchored and unanchored — ph_diagplot","title":"Diagnosis plot of proportional hazard assumption for anchored and unanchored — ph_diagplot","text":"Diagnosis plot proportional hazard assumption anchored unanchored","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnosis plot of proportional hazard assumption for anchored and unanchored — ph_diagplot","text":"","code":"ph_diagplot(   weights_object,   tte_ipd,   tte_pseudo_ipd,   trt_ipd,   trt_agd,   trt_common = NULL,   trt_var_ipd = \"ARM\",   trt_var_agd = \"ARM\",   endpoint_name = \"Time to Event Endpoint\",   time_scale,   zph_transform = \"log\",   zph_log_hazard = TRUE )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnosis plot of proportional hazard assumption for anchored and unanchored — ph_diagplot","text":"weights_object object returned estimate_weight tte_ipd data frame individual patient data (IPD) internal trial, contain least \"USUBJID\", \"EVENT\", \"TIME\" columns column indicating treatment assignment tte_pseudo_ipd data frame pseudo IPD digitized KM curves external trial (time--event endpoint), contain least \"EVENT\", \"TIME\" trt_ipd string, name interested investigation arm internal trial dat_igd (real IPD) trt_agd string, name interested investigation arm external trial dat_pseudo (pseudo IPD) trt_common string, name common comparator internal external trial, default NULL, indicating unanchored case trt_var_ipd string, column name dat_ipd contains treatment assignment trt_var_agd string, column name dat_ipd contains treatment assignment endpoint_name string, name time event endpoint, show last line title time_scale string, time unit median survival time, taking value 'years', 'months', 'weeks' 'days' zph_transform string, pass survival::cox.zph, default \"log\" zph_log_hazard logical, TRUE (default), y axis time dependent hazard function log-hazard, otherwise, hazard.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnosis plot of proportional hazard assumption for anchored and unanchored — ph_diagplot","text":"3 2 plot, include log-cumulative hazard plot, time dependent hazard function unscaled Schoenfeld residual plot, matching","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot_lch.html","id":null,"dir":"Reference","previous_headings":"","what":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"plot also known log negative log survival rate.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot_lch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"","code":"ph_diagplot_lch(   km_fit,   time_scale,   log_time = TRUE,   endpoint_name = \"\",   subtitle = \"\",   exclude_censor = TRUE )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot_lch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"km_fit returned object survival::survfit time_scale character string, 'years', 'months', 'weeks' 'days', time unit median survival time log_time logical, TRUE (default) FALSE endpoint_name character string, name endpoint subtitle character string, subtitle plot exclude_censor logical, censored data point plotted","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot_lch.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"plot log cumulative hazard rate","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot_lch.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"diagnosis plot proportional hazard assumption, versus log-time (default) time","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot_lch.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"","code":"library(survival) load(system.file(\"extdata\", \"combined_data_tte.rda\", package = \"maicplus\", mustWork = TRUE)) kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data_tte, conf.type = \"log-log\") ph_diagplot_lch(kmobj,   time_scale = \"month\", log_time = TRUE,   endpoint_name = \"OS\", subtitle = \"(Before Matching)\" )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot_schoenfeld.html","id":null,"dir":"Reference","previous_headings":"","what":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","title":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","text":"PH Diagnosis Plot Schoenfeld residuals Cox model fit","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot_schoenfeld.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","text":"","code":"ph_diagplot_schoenfeld(   coxobj,   time_scale = \"months\",   log_time = TRUE,   endpoint_name = \"\",   subtitle = \"\" )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot_schoenfeld.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","text":"coxobj object returned coxph time_scale character string, 'years', 'months', 'weeks' 'days', time unit median survival time log_time logical, TRUE (default) FALSE endpoint_name character string, name endpoint subtitle character string, subtitle plot","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot_schoenfeld.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","text":"plot Schoenfeld residuals","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/ph_diagplot_schoenfeld.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","text":"","code":"library(survival) load(system.file(\"extdata\", \"combined_data_tte.rda\", package = \"maicplus\", mustWork = TRUE)) unweighted_cox <- coxph(Surv(TIME, EVENT == 1) ~ ARM, data = combined_data_tte) ph_diagplot_schoenfeld(unweighted_cox,   time_scale = \"month\", log_time = TRUE,   endpoint_name = \"OS\", subtitle = \"(Before Matching)\" )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/plot_weights_base.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot MAIC weights in a histogram with key statistics in legend — plot_weights_base","title":"Plot MAIC weights in a histogram with key statistics in legend — plot_weights_base","text":"Generates base R histogram weights. Default plot either unscaled scaled weights .","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/plot_weights_base.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot MAIC weights in a histogram with key statistics in legend — plot_weights_base","text":"","code":"plot_weights_base(   weighted_data,   bin_col,   vline_col,   main_title,   scaled_weights )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/plot_weights_base.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot MAIC weights in a histogram with key statistics in legend — plot_weights_base","text":"weighted_data object returned calculating weights using estimate_weights bin_col string, color bins histogram vline_col string, color vertical line histogram main_title title plot scaled_weights indicator using scaled weights instead regular weights","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/plot_weights_base.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot MAIC weights in a histogram with key statistics in legend — plot_weights_base","text":"plot unscaled scaled weights","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/plot_weights_ggplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot MAIC weights in a histogram with key statistics in legend using ggplot2 — plot_weights_ggplot","title":"Plot MAIC weights in a histogram with key statistics in legend using ggplot2 — plot_weights_ggplot","text":"Generates ggplot histogram weights. Default plot unscaled scaled weights graph.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/plot_weights_ggplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot MAIC weights in a histogram with key statistics in legend using ggplot2 — plot_weights_ggplot","text":"","code":"plot_weights_ggplot(weighted_data, bin_col, vline_col, main_title, bins)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/plot_weights_ggplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot MAIC weights in a histogram with key statistics in legend using ggplot2 — plot_weights_ggplot","text":"weighted_data object returned calculating weights using estimate_weights bin_col string, color bins histogram vline_col string, color vertical line histogram main_title Name scaled weights plot unscaled weights plot, respectively. bins number bin parameter use","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/plot_weights_ggplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot MAIC weights in a histogram with key statistics in legend using ggplot2 — plot_weights_ggplot","text":"plot unscaled scaled weights","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/process_agd.html","id":null,"dir":"Reference","previous_headings":"","what":"Pre-process aggregate data — process_agd","title":"Pre-process aggregate data — process_agd","text":"function checks format aggregate data. Data required three columns: STUDY, ARM, N. Column names legal suffixes (MEAN, MEDIAN, SD, COUNT, PROP) dropped. variable count variable, converted proportions dividing sample size (N). Note, count specified, proportion always calculated based count, , specified proportion ignored applicable. aggregated data comes multiple sources (.e. different analysis population) sample size differs variable, one option specify proportion directly instead count using suffix _PROP.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/process_agd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pre-process aggregate data — process_agd","text":"","code":"process_agd(raw_agd)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/process_agd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pre-process aggregate data — process_agd","text":"raw_agd raw aggregate data contain STUDY, ARM, N. Variable names followed legal suffixes (.e. MEAN, MEDIAN, SD, COUNT, PROP).","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/process_agd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pre-process aggregate data — process_agd","text":"pre-processed aggregate level data","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/process_agd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pre-process aggregate data — process_agd","text":"","code":"target_pop <- read.csv(system.file(\"extdata\", \"aggregate_data_example_1.csv\",   package = \"maicplus\", mustWork = TRUE )) target_pop2 <- read.csv(system.file(\"extdata\", \"aggregate_data_example_2.csv\",   package = \"maicplus\", mustWork = TRUE )) target_pop3 <- read.csv(system.file(\"extdata\", \"aggregate_data_example_3.csv\",   package = \"maicplus\", mustWork = TRUE ))  target_pop <- process_agd(target_pop) target_pop2 <- process_agd(target_pop2) target_pop3 <- process_agd(target_pop3)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/pseudo_ipd_sat.html","id":null,"dir":"Reference","previous_headings":"","what":"Pseudo individual patient survival data from published study — pseudo_ipd_sat","title":"Pseudo individual patient survival data from published study — pseudo_ipd_sat","text":"Pseudo individual patient survival data published study","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/pseudo_ipd_sat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pseudo individual patient survival data from published study — pseudo_ipd_sat","text":"","code":"pseudo_ipd_sat"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/pseudo_ipd_sat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pseudo individual patient survival data from published study — pseudo_ipd_sat","text":"data frame 300 rows 3 columns: Time Survival time days. Event Event indicator 0/1. ARM Assigned treatment arm, \"B\".","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/pseudo_ipd_twt.html","id":null,"dir":"Reference","previous_headings":"","what":"Pseudo individual patient survival data from published two arm study — pseudo_ipd_twt","title":"Pseudo individual patient survival data from published two arm study — pseudo_ipd_twt","text":"Pseudo individual patient survival data published two arm study","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/pseudo_ipd_twt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pseudo individual patient survival data from published two arm study — pseudo_ipd_twt","text":"","code":"pseudo_ipd_twt"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/pseudo_ipd_twt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pseudo individual patient survival data from published two arm study — pseudo_ipd_twt","text":"data frame 800 rows 3 columns: Time Survival time days. Event Event indicator 0/1. ARM Assigned treatment arm, \"B\", \"C\".","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/reference/report_table_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"helper function: sort out a nice report table to summarize binary analysis results — report_table_binary","title":"helper function: sort out a nice report table to summarize binary analysis results — report_table_binary","text":"helper function: sort nice report table summarize binary analysis results","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/report_table_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"helper function: sort out a nice report table to summarize binary analysis results — report_table_binary","text":"","code":"report_table_binary(   binobj,   weighted_result = NULL,   eff_measure = c(\"OR\", \"RD\", \"RR\"),   tag = NULL )"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/report_table_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"helper function: sort out a nice report table to summarize binary analysis results — report_table_binary","text":"binobj object glm() weighted_result object res_AB eff_measure string, binary effect measure, \"\", \"RR\", \"RD\" tag string, default NULL, specified, extra 1st column created output","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/report_table_binary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"helper function: sort out a nice report table to summarize binary analysis results — report_table_binary","text":"data frame sample size, incidence rate, estimate binary effect measure 95% CI Wald test hazard ratio","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/report_table_tte.html","id":null,"dir":"Reference","previous_headings":"","what":"helper function: sort out a nice report table to summarize survival analysis results — report_table_tte","title":"helper function: sort out a nice report table to summarize survival analysis results — report_table_tte","text":"helper function: sort nice report table summarize survival analysis results","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/report_table_tte.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"helper function: sort out a nice report table to summarize survival analysis results — report_table_tte","text":"","code":"report_table_tte(coxobj, medSurvobj, tag = NULL)"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/report_table_tte.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"helper function: sort out a nice report table to summarize survival analysis results — report_table_tte","text":"coxobj returned object coxph medSurvobj returned object medSurv_makeup tag string, default NULL, specified, extra 1st column created output","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/report_table_tte.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"helper function: sort out a nice report table to summarize survival analysis results — report_table_tte","text":"data frame sample size, incidence rate, median survival time 95% CI, hazard ratio estimate 95% CI Wald test hazard ratio","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/survfit_makeup.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","title":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","text":"Helper function select set variables used Kaplan-Meier plot","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/survfit_makeup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","text":"","code":"survfit_makeup(km_fit, single_trt_name = \"treatment\")"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/survfit_makeup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","text":"km_fit returned object survival::survfit single_trt_name name treatment strata specified km_fit","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/survfit_makeup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","text":"list data frames variables survival::survfit(). Data frame divided treatment.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/survfit_makeup.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","text":"","code":"if (FALSE) { load(system.file(\"extdata\", \"combined_data_tte.rda\", package = \"maicplus\", mustWork = TRUE)) kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data_tte, conf.type = \"log-log\") survfit_makeup(kmobj) }"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/time_conversion.html","id":null,"dir":"Reference","previous_headings":"","what":"Get and Set Time Conversion Factors — set_time_conversion","title":"Get and Set Time Conversion Factors — set_time_conversion","text":"Get Set Time Conversion Factors","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/time_conversion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get and Set Time Conversion Factors — set_time_conversion","text":"","code":"set_time_conversion(days = 1, weeks = 7, months = 365.25/12, years = 365.25)  get_time_conversion(factor = c(\"days\", \"weeks\", \"months\", \"years\"))"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/time_conversion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get and Set Time Conversion Factors — set_time_conversion","text":"days Factor divide data time units get time days weeks Factor divide data time units get time weeks months Factor divide data time units get time months years Factor divide data time units get time years factor Time factor get.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/time_conversion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get and Set Time Conversion Factors — set_time_conversion","text":"value returned. Conversion factors stored internally used within functions.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/time_conversion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get and Set Time Conversion Factors — set_time_conversion","text":"","code":"# Native time format is years set_time_conversion(days = 1 / 365.25, weeks = 1 / 52.17857, months = 1 / 12, years = 1)  # Native time format is days set_time_conversion(days = 1, weeks = 7, months = 365.25 / 12, years = 365.25) # Get time scale factors: get_time_conversion(\"years\") #>  years  #> 365.25  get_time_conversion(\"weeks\") #> weeks  #>     7"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/weighted_sat.html","id":null,"dir":"Reference","previous_headings":"","what":"Weighted object for single arm trial data — weighted_sat","title":"Weighted object for single arm trial data — weighted_sat","text":"Weighted object single arm trial data","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/reference/weighted_sat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Weighted object for single arm trial data — weighted_sat","text":"","code":"weighted_sat"},{"path":"https://hta-pharma.github.io/maicplus/main/reference/weighted_sat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Weighted object for single arm trial data — weighted_sat","text":"maicplus_estimate_weights object created estimate_weights() containing data patient level data weights centered_colnames Columns used MAIC nr_missing Number observations missing data ess Expected sample size opt Information optim weight calculation boot Parameters bootstrap sample weights, NULL object","code":""},{"path":[]},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/main/news/index.html","id":"new-features-0-1-0","dir":"Changelog","previous_headings":"","what":"New features","title":"maicplus 0.1.0","text":"Add initializer script.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/news/index.html","id":"enhancements-0-1-0","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"maicplus 0.1.0","text":"Documentation use initialize package.","code":""},{"path":"https://hta-pharma.github.io/maicplus/main/news/index.html","id":"bug-fixes-0-1-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"maicplus 0.1.0","text":"None.","code":""}]
+[{"path":"https://hta-pharma.github.io/maicplus/articles/using_maicplus.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Using maicplus","text":"package describes steps required perform matching-adjusted indirect comparison (MAIC) analysis using maicplus package R endpoint interest either time--event (e.g. overall survival) binary (e.g. objective tumor response). methods described document based originally described Signorovitch et al. 2010 described National Institute Health Care Excellence (NICE) Decision Support Unit (DSU) Technical Support Document (TSD) 18. [signorovitch2010; phillippo2016a] MAIC methods often required : common comparator treatment link clinical trial new intervention clinical trials treatments given disease area. example study new intervention single arm trial control group. commonly referred unanchored MAIC. common comparator available link clinical trial new intervention clinical trial one treatment given disease area substantial differences patient demographic disease characteristics believed treatment effect modifiers. commonly referred anchored MAIC. premise MAIC methods adjust -trial differences patient demographic disease characteristics baseline. common treatment comparator ‘linked network’ unavailable, MAIC assumes differences absolute outcomes observed trial entirely explained imbalances prognostic variables treatment effect modifiers. Prognostic variables predictive disease outcomes, independent treatment received. example, older patients may increased risk death compared younger patients. Treatment effect modifiers variables influence relative effect one treatment compared another. example patients better performance status may experience larger treatment benefit worse performance status. assumption, every prognostic variable every treatment effect modifier imbalanced two studies must available. assumption generally considered difficult meet. [phillippo2016a] several ways identifying prognostic variables/treatment effect modifiers used MAIC analyses, include: Clinical expertise (available project) Published papers/previous submissions (identified disease area previously) Univariable/multivariable regression analyses identify covariates significant effect outcome Subgroup analyses clinical trials may identify interactions patient characteristics relative treatment effect","code":""},{"path":"https://hta-pharma.github.io/maicplus/articles/using_maicplus.html","id":"theory-behind-maic","dir":"Articles","previous_headings":"","what":"Theory behind MAIC","title":"Using maicplus","text":"briefly go theory behind MAIC. detailed information, see Signorovitch et al. 2010. Let us define \\(t_i\\) treatment patient \\(\\) received. assume \\(t_i=0\\) patient received intervention (IPD) \\(t_i=1\\) patient received comparator treatment. causal effect treatment \\(T=0\\) vs \\(T=1\\) mean outcome \\(Y\\) can estimated \\[ \\frac{\\sum_{=1}^{n}y_{}(1-t_{})w_{}}{\\sum_{=1}^{n}(1-t_{})w_{}}-\\bar{y}_{1} \\] \\(w_i=\\frac{Pr(T_i=1\\mid x_i)}{Pr(T_i=0\\mid x_i)}\\) odds patient \\(\\) received treatment \\(T=1\\) vs \\(T=0\\) (.e. enrolls aggregate data study vs IPD study) given baseline characteristics \\(x_i\\). Thus, patients receiving \\(T=0\\) re-weighted match distribution patients receiving \\(T=1\\). Note causal effect case outcome \\(Y\\) continuous. outcome binary, \\(Y\\) proportion use link function logit give us causal effect odds ratio scale. propensity score methods, may assume \\(w_i\\) follow logistic regression form \\[ w_{}=exp(x_i^{T}\\beta) \\] However, order estimate \\(\\beta\\), use maximum likelihood approach IPD trials. Instead, use method moments. estimate \\(\\beta\\) weighted averages covariates IPD exactly matches aggregate data averages. Mathematically speaking, want estimate \\(\\beta\\) : \\[ 0=\\frac{\\sum_{=1}^{n}x_{}exp(x_i^{T}\\hat{\\beta})}{\\sum_{=1}^{n}exp(x_i^{T}\\hat{\\beta})}-\\bar{x}_{agg} \\] \\(x_i\\) contains confounders logistic regression \\(w_i\\) correctly specified, obtain consistent estimate causal effect intervention vs comparator treatment. equation equivalent \\[ 0=\\sum_{=1}^{n}(x_{}-\\bar{x}_{agg})exp(x_{}^{T}\\hat{\\beta}) \\] transform transform IPD subtracting aggregate data means (centering needed preprocessing). \\[ 0=\\sum_{=1}^{n}x_{}exp(x_{}^{T}\\hat{\\beta}) \\] Note first derivative \\[ Q(\\beta)=\\sum_{=1}^{n}exp(x_{}^{T}\\hat{\\beta}) \\] second derivative \\[ Q''(\\beta)=\\sum_{=1}^{n}x_ix_i^Texp(x_{}^{T}\\hat{\\beta}) \\] Since \\(Q''(\\beta)\\) positive-definite \\(\\beta\\), \\(Q(\\beta)\\) convex finite solution equation unique corresponds global minimum \\(Q(\\beta)\\). Thus, can use optimization methods calculate \\(\\beta\\).","code":""},{"path":"https://hta-pharma.github.io/maicplus/articles/using_maicplus.html","id":"example-scenario","dir":"Articles","previous_headings":"","what":"Example scenario","title":"Using maicplus","text":"present unanchored MAIC two treatments lung cancer. two endpoints compared overall survival (time event outcome) objective response (binary outcome). data available : Individual patient data single arm study Aggregate summary data comparator study Pseudo patient data comparator study. required matching process needed derive relative treatment effects internal treatment comparator treatment.","code":""},{"path":"https://hta-pharma.github.io/maicplus/articles/using_maicplus.html","id":"preprocessing","dir":"Articles","previous_headings":"","what":"Preprocessing","title":"Using maicplus","text":"Additional suggested packages vignette:","code":"library(maicplus) library(dplyr) # this is used for data merging/cleaning. Package itself does not depend on dplyr #>  #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #>  #>     filter, lag #> The following objects are masked from 'package:base': #>  #>     intersect, setdiff, setequal, union # library(clubSandwich) # For robust standard error in logistic regression library(sandwich)  library(survival) library(survminer) # for ggsurvplot #> Loading required package: ggplot2 #> Loading required package: ggpubr #>  #> Attaching package: 'survminer' #> The following object is masked from 'package:survival': #>  #>     myeloma library(ggplot2) # for ggplot functions library(boot) # for bootstrapping #>  #> Attaching package: 'boot' #> The following object is masked from 'package:survival': #>  #>     aml"},{"path":"https://hta-pharma.github.io/maicplus/articles/using_maicplus.html","id":"preprocessing-ipd","dir":"Articles","previous_headings":"Preprocessing","what":"Preprocessing IPD","title":"Using maicplus","text":"example scenario, age, sex, Eastern Cooperative Oncology Group (ECOG) performance status, smoking status, number previous treatments identified imbalanced prognostic variables/treatment effect modifiers. example reads combines data three standard simulated data sets (adsl, adrs adtte) saved ‘.csv’ files.","code":"adsl <- read.csv(system.file(\"extdata\", \"adsl.csv\",   package = \"maicplus\",   mustWork = TRUE )) adrs <- read.csv(system.file(\"extdata\", \"adrs.csv\",   package = \"maicplus\",   mustWork = TRUE )) adtte <- read.csv(system.file(\"extdata\", \"adtte.csv\", package = \"maicplus\", mustWork = TRUE))  # Data containing the matching variables adsl <- adsl %>%   mutate(SEX_MALE = ifelse(SEX == \"Male\", 1, 0)) %>%   mutate(AGE_SQUARED = AGE^2)  # Could use built-in function for dummizing variables # adsl <- dummize_ipd(adsl, dummize_cols=c(\"SEX\"), dummize_ref_level=c(\"Female\"))  # Response data adrs <- adrs %>%   filter(PARAM == \"Response\") %>%   transmute(USUBJID, ARM, RESPONSE = AVAL)  # Time to event data (overall survival) adtte <- adtte %>%   filter(PARAMCD == \"OS\") %>%   mutate(EVENT = 1 - CNSR) %>%   transmute(USUBJID, ARM, TIME = AVAL, EVENT)  # Combine all ipd data ipd <- adsl %>%   full_join(adrs, by = c(\"USUBJID\", \"ARM\")) %>%   full_join(adtte, by = c(\"USUBJID\", \"ARM\")) head(ipd) #>   X USUBJID ARM AGE    SEX SMOKE ECOG0 N_PR_THER SEX_MALE AGE_SQUARED RESPONSE #> 1 1       1   A  45   Male     0     0         4        1        2025        0 #> 2 2       2   A  71   Male     0     0         3        1        5041        1 #> 3 3       3   A  58   Male     1     1         2        1        3364        1 #> 4 4       4   A  48 Female     0     1         4        0        2304        1 #> 5 5       5   A  69   Male     0     1         4        1        4761        0 #> 6 6       6   A  48 Female     0     1         4        0        2304        0 #>       TIME EVENT #> 1 281.5195     0 #> 2 500.0000     0 #> 3 304.6406     0 #> 4 102.4731     0 #> 5 101.6632     0 #> 6 237.0593     1"},{"path":"https://hta-pharma.github.io/maicplus/articles/using_maicplus.html","id":"preprocessing-aggregate-data","dir":"Articles","previous_headings":"Preprocessing","what":"Preprocessing aggregate data","title":"Using maicplus","text":"two ways specifying aggregate data. One approach read aggregate data using excel spreadsheet. spreadsheet, possible variable types include mean, median, standard deviation continuous variables count proportion binary variables. naming followed suffixes accordingly: _COUNT, _MEAN, _MEDIAN, _SD, _PROP. , process_agd convert count proportions. way define data frame aggregate data R. way, _COUNT prefix specified proportion allowed binary variables. suffix names first approach. Possible missingness binary variables accounted subtracting denominator missing count .e. proportion = count / (N - missing).","code":"# Through excel spreadsheet # target_pop <- read.csv(system.file(\"extdata\",\"aggregate_data_example_1.csv\", package = \"maicplus\", mustWork = TRUE)) # agd <- process_agd(target_pop)  # Second approach by defining a data frame in R agd <- data.frame(   STUDY = \"Lung study\",   ARM = \"Total\",   N = 300,   AGE_MEAN = 51,   AGE_MEDIAN = 49,   AGE_SD = 3.25,   SEX_MALE_PROP = 147 / 300,   ECOG0_PROP = 0.40,   SMOKE_PROP = 58 / (300 - 5),   N_PR_THER_MEDIAN = 2 )"},{"path":"https://hta-pharma.github.io/maicplus/articles/using_maicplus.html","id":"preprocessing-aggregate-data-1","dir":"Articles","previous_headings":"Preprocessing","what":"Preprocessing aggregate data","title":"Using maicplus","text":"","code":"#### prepare data ipd_centered <- center_ipd(ipd = ipd, agd = agd) head(ipd_centered) #>   X USUBJID ARM AGE    SEX SMOKE ECOG0 N_PR_THER SEX_MALE AGE_SQUARED RESPONSE #> 1 1       1   A  45   Male     0     0         4        1        2025        0 #> 2 2       2   A  71   Male     0     0         3        1        5041        1 #> 3 3       3   A  58   Male     1     1         2        1        3364        1 #> 4 4       4   A  48 Female     0     1         4        0        2304        1 #> 5 5       5   A  69   Male     0     1         4        1        4761        0 #> 6 6       6   A  48 Female     0     1         4        0        2304        0 #>       TIME EVENT AGE_CENTERED AGE_MEDIAN_CENTERED AGE_SQUARED_CENTERED #> 1 281.5195     0           -6                -0.5            -586.5625 #> 2 500.0000     0           20                 0.5            2429.4375 #> 3 304.6406     0            7                 0.5             752.4375 #> 4 102.4731     0           -3                -0.5            -307.5625 #> 5 101.6632     0           18                 0.5            2149.4375 #> 6 237.0593     1           -3                -0.5            -307.5625 #>   SEX_MALE_CENTERED ECOG0_CENTERED SMOKE_CENTERED N_PR_THER_MEDIAN_CENTERED #> 1              0.51           -0.4     -0.1966102                       0.5 #> 2              0.51           -0.4     -0.1966102                       0.5 #> 3              0.51            0.6      0.8033898                      -0.5 #> 4             -0.49            0.6     -0.1966102                       0.5 #> 5              0.51            0.6     -0.1966102                       0.5 #> 6             -0.49            0.6     -0.1966102                       0.5"},{"path":"https://hta-pharma.github.io/maicplus/articles/using_maicplus.html","id":"how-to-handle-standard-deviation-aggregate-summary","dir":"Articles","previous_headings":"Preprocessing > Preprocessing aggregate data","what":"How to handle standard deviation aggregate summary","title":"Using maicplus","text":"described Phillippo et al. 2016, balancing mean standard deviation continuous variables (possible) may considered cases. standard deviation provided comparator population, preprocessing done target population, \\(E(X^2)\\) calculated using variance formula \\(Var(X)=E(X^{2})-E(X)^{2}\\). \\(E(X^2)\\) target population matched IPD level data, \\(X^{2}\\) calculated preprocessing stage IPD.","code":""},{"path":"https://hta-pharma.github.io/maicplus/articles/using_maicplus.html","id":"how-to-handle-median-aggregate-summary","dir":"Articles","previous_headings":"Preprocessing > Preprocessing aggregate data","what":"How to handle median aggregate summary","title":"Using maicplus","text":"median provided, IPD preprocessed categorize variable binary variable. values IPD higher comparator population median assigned value 1. Conversely, values lower assigned value 0. Comparator population median replaced 0.5 adjust categorization IPD data. newly created IPD binary variable matched proportion 0.5.","code":""},{"path":"https://hta-pharma.github.io/maicplus/articles/using_maicplus.html","id":"calculating-weights","dir":"Articles","previous_headings":"","what":"Calculating weights","title":"Using maicplus","text":"Another check weights calculated look weighted covariates match aggregate data summary.","code":"# list variables that are going to be used to match centered_colnames <- c(\"AGE\", \"AGE_SQUARED\", \"SEX_MALE\", \"ECOG0\", \"SMOKE\", \"N_PR_THER_MEDIAN\") centered_colnames <- paste0(centered_colnames, \"_CENTERED\")  weighted_data <- estimate_weights(   data = ipd_centered,   centered_colnames = centered_colnames )  # Alternatively, you can specify the numeric column locations for centered_colnames # weighted_data <- estimate_weights(ipd_centered, centered_colnames = c(14, 16:20))  # Two options to plot weights plot using base R or ggplot plot(weighted_data) plot(weighted_data, ggplot = TRUE) outdata <- check_weights(weighted_data, agd) outdata #>   covariate match_stat internal_trial internal_trial_after_weighted #> 1       AGE       Mean         59.850                         51.00 #> 2       AGE         SD          9.011                          3.25 #> 3  SEX_MALE       Prop          0.380                          0.49 #> 4     ECOG0       Prop          0.410                          0.40 #> 5     SMOKE       Prop          0.320                          0.20 #> 6 N_PR_THER     Median          3.000                          2.00 #>   external_trial sum_centered_IPD_with_weights #> 1          51.00                        0.0001 #> 2           3.25                        0.0125 #> 3           0.49                        0.0000 #> 4           0.40                        0.0000 #> 5           0.20                        0.0000 #> 6           2.00                        0.0000"},{"path":"https://hta-pharma.github.io/maicplus/articles/using_maicplus.html","id":"time-to-event-analysis","dir":"Articles","previous_headings":"","what":"Time to event analysis","title":"Using maicplus","text":"first need combine internal IPD data pseudo comparator IPD. obtain pseudo comparator IPD, digitize Kaplan Meier curves comparator study.","code":"pseudo_ipd <- read.csv(system.file(\"extdata\", \"psuedo_IPD.csv\", package = \"maicplus\", mustWork = TRUE)) ipd_matched <- weighted_data$data  # Need to specify pseudo_ipd ARM if not specified pseudo_ipd$ARM <- \"B\" # Need to specify ARM for pseudo ipd  # Need to specify weights for pseudo_ipd which is 1 pseudo_ipd$weights <- 1  # make sure pseudo_ipd name has same name compared with ipd_matched for time, event, arm colnames(pseudo_ipd) <- c(\"TIME\", \"EVENT\", \"ARM\", \"weights\")  combined_data_tte <- rbind(   ipd_matched[, colnames(pseudo_ipd)],   pseudo_ipd )  head(combined_data_tte) #>       TIME EVENT ARM      weights #> 1 281.5195     0   A 6.884028e-01 #> 2 500.0000     0   A 5.669105e-08 #> 3 304.6406     0   A 1.146355e-01 #> 4 102.4731     0   A 1.178307e+00 #> 5 101.6632     0   A 1.605203e-06 #> 6 237.0593     1   A 1.178307e+00"},{"path":"https://hta-pharma.github.io/maicplus/articles/using_maicplus.html","id":"report-1-kaplan-meier-plot","dir":"Articles","previous_headings":"Time to event analysis","what":"Report 1: Kaplan-Meier plot","title":"Using maicplus","text":"also \"ggplot\" option Kaplan-Meier curves using survminer R package.","code":"kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data_tte, conf.type = \"log-log\") kmobj_adj <- survfit(Surv(TIME, EVENT) ~ ARM,   combined_data_tte,   weights = combined_data_tte$weights, conf.type = \"log-log\" )  par(cex.main = 0.85)  kmdat <- do.call(rbind, survfit_makeup(kmobj)) kmdat$treatment <- factor(kmdat$treatment) basic_kmplot(kmdat,   time_scale = \"month\",   time_grid = seq(0, 20, by = 2),   show_risk_set = FALSE,   main_title = \"Kaplan-Meier Curves\",   subplot_heights = NULL,   suppress_plot_layout = FALSE,   use_colors = NULL,   use_line_types = NULL ) # km_plot2(combined_data_tte, trt = \"A\", trt_ext = \"B\", censor = TRUE, risk.table = TRUE)"},{"path":"https://hta-pharma.github.io/maicplus/articles/using_maicplus.html","id":"report-2-analysis-table-cox-model-before-and-after-matching-incl-median-survival-time","dir":"Articles","previous_headings":"Time to event analysis","what":"Report 2: Analysis table (Cox model) before and after matching, incl Median Survival Time","title":"Using maicplus","text":"can fit cox regression model using combined dataset. weight adjusted cox regression, fit model robust standard errors. Along hazard ratios, can also find median survival time using medSurv_makeup function. , report_table_tte function nicely combines information together create result table.","code":"# Fit a Cox model with/without weights to estimate the HR unweighted_cox <- coxph(Surv(TIME, EVENT == 1) ~ ARM, data = combined_data_tte) weighted_cox <- coxph(Surv(TIME, EVENT == 1) ~ ARM,   data = combined_data_tte, weights = combined_data_tte$weights, robust = TRUE )  # Derive median survival time medSurv <- medSurv_makeup(kmobj, legend = \"before matching\", time_scale = \"day\") medSurv_adj <- medSurv_makeup(kmobj_adj, legend = \"after matching\", time_scale = \"day\") medSurv_out <- rbind(medSurv, medSurv_adj) medSurv_out #>   treatment            type records    n.max  n.start    events    rmean #> 1     ARM=A before matching     500 500.0000 500.0000 190.00000 265.1012 #> 2     ARM=B before matching     300 300.0000 300.0000 178.00000 130.9893 #> 3     ARM=A  after matching     500 199.8422 199.8422  66.84953 307.7223 #> 4     ARM=B  after matching     300 300.0000 300.0000 178.00000 130.9893 #>   se(rmean)    median   0.95LCL  0.95UCL #> 1  10.80981 230.94839 191.10767 313.1574 #> 2  10.24910  83.58535  68.82298 101.0786 #> 3  16.71338 362.20670 237.05932 452.7209 #> 4  10.24910  83.58535  68.82298 101.0786 rbind(   report_table_tte(unweighted_cox, medSurv, tag = paste0(\"Before/\", \"Overall survival\")),   report_table_tte(weighted_cox, medSurv_adj, tag = paste0(\"After/\", \"Overall survival\")) ) #>                   Matching treatment     N n.events(%)     median[95% CI] #> 2  Before/Overall survival     ARM=B 300.0   178(59.3)  83.6[ 68.8;101.1] #> 1                              ARM=A 500.0   190(38.0) 230.9[191.1;313.2] #> 21  After/Overall survival     ARM=B 300.0   178(59.3)  83.6[ 68.8;101.1] #> 11                             ARM=A 199.8  66.8(33.5) 362.2[237.1;452.7] #>         HR[95% CI] p-Value #> 2  2.67[2.16;3.29]  <0.001 #> 1                          #> 21 3.46[2.53;4.74]  <0.001 #> 11"},{"path":"https://hta-pharma.github.io/maicplus/articles/using_maicplus.html","id":"report-3-bootstrap-result","dir":"Articles","previous_headings":"Time to event analysis","what":"Report 3: Bootstrap result","title":"Using maicplus","text":"","code":"set.seed(1) HR_bootstraps <- boot(   data = ipd_centered,   statistic = bootstrap_HR,   centered_colnames = centered_colnames,   pseudo_ipd = pseudo_ipd,   model = Surv(TIME, EVENT == 1) ~ ARM,   ref_treat = \"B\",   R = 1000 ) # Median of the bootstrap samples HR_median <- median(HR_bootstraps$t)  # Bootstrap CI - Percentile CI boot_ci_HR <- boot.ci(boot.out = HR_bootstraps, index = 1, type = \"perc\")  # Bootstrap CI - BCa CI boot_ci_HR_BCA <- boot.ci(boot.out = HR_bootstraps, index = 1, type = \"bca\")  HR_median #> [1] 0.2858165 boot_ci_HR #> BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS #> Based on 1000 bootstrap replicates #>  #> CALL :  #> boot.ci(boot.out = HR_bootstraps, type = \"perc\", index = 1) #>  #> Intervals :  #> Level     Percentile      #> 95%   ( 0.2236,  0.3689 )   #> Calculations and Intervals on Original Scale boot_ci_HR_BCA #> BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS #> Based on 1000 bootstrap replicates #>  #> CALL :  #> boot.ci(boot.out = HR_bootstraps, type = \"bca\", index = 1) #>  #> Intervals :  #> Level       BCa           #> 95%   ( 0.2296,  0.3789 )   #> Calculations and Intervals on Original Scale"},{"path":"https://hta-pharma.github.io/maicplus/articles/using_maicplus.html","id":"report-4-diagnosis-plot","dir":"Articles","previous_headings":"Time to event analysis","what":"Report 4: Diagnosis Plot","title":"Using maicplus","text":"","code":"# grambsch & theaneu ph test coxdiag <- cox.zph(unweighted_cox, global = FALSE, transform = \"log\") coxdiag_adj <- cox.zph(weighted_cox, global = FALSE, transform = \"log\")  coxdiag #>       chisq df    p #> ARM 0.00996  1 0.92 par(mfrow = c(1, 1), tcl = -0.15) plot(coxdiag, yaxt = \"n\", main = \"Grambsch & Terneau Plot (before matching)\") axis(2, las = 1) coxdiag_adj #>      chisq df    p #> ARM 0.0438  1 0.83 par(mfrow = c(1, 1), tcl = -0.15) plot(coxdiag_adj, yaxt = \"n\", main = \"Grambsch & Terneau Plot (after matching)\") axis(2, las = 1) # log-cumulative hazard plot ph_diagplot_lch(kmobj,   time_scale = \"month\", log_time = TRUE,   endpoint_name = \"OS\", subtitle = \"(Before Matching)\" ) ph_diagplot_lch(kmobj_adj,   time_scale = \"month\", log_time = TRUE,   endpoint_name = \"OS\", subtitle = \"(After Matching)\" ) # schoenfeld residual plot ph_diagplot_schoenfeld(unweighted_cox,   time_scale = \"month\", log_time = TRUE,   endpoint_name = \"OS\", subtitle = \"(Before Matching)\" ) ph_diagplot_schoenfeld(weighted_cox,   time_scale = \"month\", log_time = TRUE,   endpoint_name = \"OS\", subtitle = \"(After Matching)\" )"},{"path":"https://hta-pharma.github.io/maicplus/articles/using_maicplus.html","id":"analysis-using-a-built-in-wrapper","dir":"Articles","previous_headings":"Time to event analysis","what":"Analysis using a built-in wrapper","title":"Using maicplus","text":"One can analysis wrapper","code":"# put in wrapper code"},{"path":"https://hta-pharma.github.io/maicplus/articles/using_maicplus.html","id":"binary-outcome-analysis-todo","dir":"Articles","previous_headings":"","what":"Binary outcome analysis (TODO)","title":"Using maicplus","text":"","code":"# Simulate response data based on the known proportion of responders comparator_prop_events <- 0.4  # Calculate number with event. Use round() to ensure we end up with a whole number of people. # number without an event = Total N - number with event to ensure we keep the same number of patients n_with_event <- round(agd$N * comparator_prop_events, digits = 0) comparator_binary <- data.frame(\"RESPONSE\" = c(rep(1, n_with_event), rep(0, agd$N - n_with_event))) comparator_binary$ARM <- \"B\" # need to specify ARM for comparator data ipd_matched <- weighted_data$data  combined_data_binary <- merge_two_data(comparator_binary, ipd_matched, internal_response_name = \"RESPONSE\")  unweighted_OR <- glm(   formula = RESPONSE ~ ARM,   family = binomial(link = \"logit\"),   data = combined_data_binary )  # Log odds ratio log_OR_CI <- cbind(coef(unweighted_OR), confint.default(unweighted_OR, level = 0.95))[2, ]  # Odds ratio OR_CI <- exp(log_OR_CI) names(OR_CI) <- c(\"OR\", \"OR_low_CI\", \"OR_upp_CI\") OR_CI  # Fit a logistic regression model with weights to estimate the weighted OR weighted_OR <- suppressWarnings(glm(   formula = RESPONSE ~ ARM,   family = binomial(link = \"logit\"),   data = combined_data_binary,   weight = weights ))  # Weighted log odds ratio log_OR_CI_wtd <- cbind(coef(weighted_OR), confint.default(weighted_OR, level = 0.95))[2, ]  # Weighted odds ratio OR_CI_wtd <- exp(log_OR_CI_wtd) names(OR_CI_wtd) <- c(\"OR\", \"OR_low_CI\", \"OR_upp_CI\") OR_CI_wtd  # Robust standard error if (requireNamespace(\"clubSandwich\")) {   vmod <- clubSandwich::vcovCR(weighted_OR, cluster = seq_len(dim(combined_data_binary)[1]), type = \"CR2\")   coef_res <- clubSandwich::conf_int(weighted_OR, vmod, coef = 2)    OR_CI_robust <- exp(with(coef_res, c(beta, CI_L, CI_U)))   names(OR_CI_robust) <- c(\"Estimate\", \"Lower 95% CI\", \"Upper 95% CI\")   OR_CI_robust } # Using sandwich package V.sw <- sandwich::vcovHC(weighted_OR) # white's estimator SD <- sqrt(V.sw[2, 2]) Estimate <- coef(weighted_OR)[2] OR_CI_robust2 <- exp(c(Estimate, Estimate - 1.96 * SD, Estimate + 1.96 * SD)) names(OR_CI_robust2) <- c(\"Estimate\", \"Lower 95% CI\", \"Upper 95% CI\") OR_CI_robust2"},{"path":"https://hta-pharma.github.io/maicplus/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"hta-pharma. Author, maintainer.","code":""},{"path":"https://hta-pharma.github.io/maicplus/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"hta-pharma (2024). maicplus: Matching Adjusted Indirect Comparison. R package version 0.1.0, https://github.com/hta-pharma/maicplus/.","code":"@Manual{,   title = {maicplus: Matching Adjusted Indirect Comparison},   author = {{hta-pharma}},   year = {2024},   note = {R package version 0.1.0},   url = {https://github.com/hta-pharma/maicplus/}, }"},{"path":"https://hta-pharma.github.io/maicplus/index.html","id":"maicplus","dir":"","previous_headings":"","what":"Matching Adjusted Indirect Comparison","title":"Matching Adjusted Indirect Comparison","text":"open source R package MAIC","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/adrs_sat.html","id":null,"dir":"Reference","previous_headings":"","what":"Binary outcome data from single arm trial — adrs_sat","title":"Binary outcome data from single arm trial — adrs_sat","text":"Binary outcome data single arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/adrs_sat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Binary outcome data from single arm trial — adrs_sat","text":"","code":"adrs_sat"},{"path":"https://hta-pharma.github.io/maicplus/reference/adrs_sat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Binary outcome data from single arm trial — adrs_sat","text":"data frame 500 rows 5 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm. AVAL Analysis value, dataset indicator response. PARAM Parameter type AVAL. RESPONSE Indicator response.","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/reference/adrs_twt.html","id":null,"dir":"Reference","previous_headings":"","what":"Binary outcome data from two arm trial — adrs_twt","title":"Binary outcome data from two arm trial — adrs_twt","text":"Binary outcome data two arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/adrs_twt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Binary outcome data from two arm trial — adrs_twt","text":"","code":"adrs_twt"},{"path":"https://hta-pharma.github.io/maicplus/reference/adrs_twt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Binary outcome data from two arm trial — adrs_twt","text":"data frame 1000 rows 5 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm, \"\", \"C\". AVAL Analysis value, dataset indicator response. PARAM Parameter type AVAL. RESPONSE Indicator response.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/adsl_sat.html","id":null,"dir":"Reference","previous_headings":"","what":"Patient data from single arm study — adsl_sat","title":"Patient data from single arm study — adsl_sat","text":"Patient data single arm study","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/adsl_sat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Patient data from single arm study — adsl_sat","text":"","code":"adsl_sat"},{"path":"https://hta-pharma.github.io/maicplus/reference/adsl_sat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Patient data from single arm study — adsl_sat","text":"data frame 500 rows 8 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm. AGE Age years baseline. SEX Sex patient recorded character \"Male\"/\"Female\". SMOKE Smoking status baseline integer 1/0. ECOG0 Indicator ECOG score = 0 baseline integer 1/0. N_PR_THER Number prior therapies received integer 1, 2, 3, 4. SEX_MALE Indicator SEX == \"Male\" numeric 1/0.","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/reference/adsl_twt.html","id":null,"dir":"Reference","previous_headings":"","what":"Patient data from two arm trial — adsl_twt","title":"Patient data from two arm trial — adsl_twt","text":"Patient data two arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/adsl_twt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Patient data from two arm trial — adsl_twt","text":"","code":"adsl_twt"},{"path":"https://hta-pharma.github.io/maicplus/reference/adsl_twt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Patient data from two arm trial — adsl_twt","text":"data frame 1000 rows 8 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm. AGE Age years baseline. SEX Sex patient recorded character \"Male\"/\"Female\" SMOKE Smoking status baseline integer 1/0. ECOG0 Indicator ECOG score = 0 baseline integer 1/0. N_PR_THER Number prior therapies received integer 1, 2, 3, 4. SEX_MALE Indicator SEX == \"Male\" numeric 1/0","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/reference/adtte_sat.html","id":null,"dir":"Reference","previous_headings":"","what":"Survival data from single arm trial — adtte_sat","title":"Survival data from single arm trial — adtte_sat","text":"Survival data single arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/adtte_sat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Survival data from single arm trial — adtte_sat","text":"","code":"adtte_sat"},{"path":"https://hta-pharma.github.io/maicplus/reference/adtte_sat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Survival data from single arm trial — adtte_sat","text":"data frame 500 rows 10 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm, \"\". AVAL Analysis value dataset overall survival time days. AVALU Unit AVAL. PARAMCD Paramater code AVAL, \"OS\". PARAM Parameter name AVAL, \"Overall Survival. CNSR Censoring indicator 0/1. TIME Survival time days. EVENT Event indicator 0/1.","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/reference/adtte_twt.html","id":null,"dir":"Reference","previous_headings":"","what":"Survival data from two arm trial — adtte_twt","title":"Survival data from two arm trial — adtte_twt","text":"Survival data two arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/adtte_twt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Survival data from two arm trial — adtte_twt","text":"","code":"adtte_twt"},{"path":"https://hta-pharma.github.io/maicplus/reference/adtte_twt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Survival data from two arm trial — adtte_twt","text":"data frame 1000 rows 10 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm, \"\", \"C\". AVAL Analysis value dataset overall survival time days. AVALU Unit AVAL. PARAMCD Parameter code AVAL, \"OS\". PARAM Parameter name AVAL, \"Overall Survival. CNSR Censoring indicator 0/1. TIME Survival time days. EVENT Event indicator 0/1.","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/reference/agd.html","id":null,"dir":"Reference","previous_headings":"","what":"Aggregate effect modifier data from published study — agd","title":"Aggregate effect modifier data from published study — agd","text":"data formatted used center_ipd().","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/agd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aggregate effect modifier data from published study — agd","text":"","code":"agd"},{"path":"https://hta-pharma.github.io/maicplus/reference/agd.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Aggregate effect modifier data from published study — agd","text":"data frame 3 rows 9 columns: STUDY study name, Study_XXXX ARM Study arm name total N Number observations study arm AGE_MEAN Mean age study arm AGE_MEDIAN Median age study arm AGE_SD Standard deviation age study arm SEX_MALE_COUNT Number male patients ECOG0_COUNT Number patients ECOG score = 0 SMOKE_COUNT Number smokers N_PR_THER_MEDIAN Median number prior therapies","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/reference/basic_kmplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Basic Kaplan Meier (KM) plot function — basic_kmplot","title":"Basic Kaplan Meier (KM) plot function — basic_kmplot","text":"function can generate basic KM plot without risk set table appended bottom. single plot, can include 4 KM curves. depends number levels 'treatment' column input data.frame kmdat","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/basic_kmplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Basic Kaplan Meier (KM) plot function — basic_kmplot","text":"","code":"basic_kmplot(   kmdat,   endpoint_name = \"Time to Event Endpoint\",   time_scale,   time_grid,   show_risk_set = TRUE,   main_title = \"Kaplan-Meier Curves\",   subplot_heights = NULL,   suppress_plot_layout = FALSE,   use_colors = NULL,   use_line_types = NULL,   use_pch_cex = 0.65,   use_pch_alpha = 100 )"},{"path":"https://hta-pharma.github.io/maicplus/reference/basic_kmplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Basic Kaplan Meier (KM) plot function — basic_kmplot","text":"kmdat data.frame, must consist treatment, time (unit days), n.risk, censor, surv, similar output maicplus:::survfit_makeup endpoint_name string, name time event endpoint, show last line title time_scale string, time unit median survival time, taking value 'years', 'months', 'weeks' 'days' time_grid numeric vector unit time_scale, risk set table x axis km plot defined based time grid show_risk_set logical, show risk set table , TRUE default main_title string, main title KM plot subplot_heights numeric vector, heights argument graphic::layout(),NULL default means user use default setting suppress_plot_layout logical, suppress layout setting function user can specify layout outside function, FALSE default use_colors character vector length 4, colors KM curves, passed col lines() use_line_types numeric vector length 4, line type KM curves, passed lty lines() use_pch_cex scalar 0 1, point size indicate censored individuals KM curves, passed cex points() use_pch_alpha scalar 0 255, degree color transparency points indicate censored individuals KM curves, passed cex points()","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/basic_kmplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Basic Kaplan Meier (KM) plot function — basic_kmplot","text":"KM plot without risk set table appended bottom, 4 KM curves","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/basic_kmplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Basic Kaplan Meier (KM) plot function — basic_kmplot","text":"","code":"library(survival) load(system.file(\"extdata\", \"combined_data_tte.rda\", package = \"maicplus\", mustWork = TRUE)) kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data_tte, conf.type = \"log-log\") kmdat <- do.call(rbind, survfit_makeup(kmobj)) kmdat$treatment <- factor(kmdat$treatment)  # without risk set table basic_kmplot(kmdat,   time_scale = \"month\",   time_grid = seq(0, 20, by = 2),   show_risk_set = FALSE,   main_title = \"Kaplan-Meier Curves\",   subplot_heights = NULL,   suppress_plot_layout = FALSE,   use_colors = NULL,   use_line_types = NULL )   # with risk set table basic_kmplot(kmdat,   time_scale = \"month\",   time_grid = seq(0, 20, by = 2),   show_risk_set = TRUE,   main_title = \"Kaplan-Meier Curves\",   subplot_heights = NULL,   suppress_plot_layout = FALSE,   use_colors = NULL,   use_line_types = NULL )"},{"path":"https://hta-pharma.github.io/maicplus/reference/basic_kmplot2.html","id":null,"dir":"Reference","previous_headings":"","what":"Basic Kaplan Meier (KM) plot function using ggplot — basic_kmplot2","title":"Basic Kaplan Meier (KM) plot function using ggplot — basic_kmplot2","text":"function generates basic KM plot using ggplot.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/basic_kmplot2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Basic Kaplan Meier (KM) plot function using ggplot — basic_kmplot2","text":"","code":"basic_kmplot2(   kmlist,   kmlist_name,   endpoint_name = \"Time to Event Endpoint\",   show_risk_set = TRUE,   main_title = \"Kaplan-Meier Curves\",   break_x_by = NULL,   censor = TRUE,   xlim = NULL,   use_colors = NULL,   use_line_types = NULL )"},{"path":"https://hta-pharma.github.io/maicplus/reference/basic_kmplot2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Basic Kaplan Meier (KM) plot function using ggplot — basic_kmplot2","text":"kmlist list survfit object kmlist_name vector indicating treatment names survfit object endpoint_name string, name time event endpoint, show last line title show_risk_set logical, show risk set table , TRUE default main_title string, main title KM plot break_x_by bin parameter survminer censor indicator include censor information xlim x limit x-axis plot use_colors character vector length 4, colors KM curves, passed 'col' lines() use_line_types numeric vector length 4, line type KM curves, passed lty lines()","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/bootstrap_HR.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrapping weighted hazard ratios to obtain confidence interval — bootstrap_HR","title":"Bootstrapping weighted hazard ratios to obtain confidence interval — bootstrap_HR","text":"function uses boot resample internal IPD data obtain bootstrapped hazard ratio values used form confidence interval. Note due lack full IPD covariates aggregate data, pseudo IPD bootstrapped.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/bootstrap_HR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrapping weighted hazard ratios to obtain confidence interval — bootstrap_HR","text":"","code":"bootstrap_HR(   ipd_centered,   i,   centered_colnames,   pseudo_ipd = NULL,   model,   min_weight = 1e-04,   ref_treat = NULL )"},{"path":"https://hta-pharma.github.io/maicplus/reference/bootstrap_HR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrapping weighted hazard ratios to obtain confidence interval — bootstrap_HR","text":"ipd_centered data frame containing individual patient data internal IPD study. data frame already centered using center_ipd function Index used select sample within boot. centered_colnames character vector giving names covariates use matching. names must match column names internal dataset. pseudo_ipd data frame containing pseudo individual patient data comparator study needed derive relative treatment effect. Pseudo IPD used unanchored case. pseudo_ipd time, event, ARM variable name order ipd_centered model Survival model run calculate HR correct time, event, #'   ARM names min_weight numeric value defines minimum weight allowed. value (default 0.0001) replace weights estimated 0 sample. ref_treat Reference treatment survival model","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/bootstrap_HR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrapping weighted hazard ratios to obtain confidence interval — bootstrap_HR","text":"HR bootstraps","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/bootstrap_HR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrapping weighted hazard ratios to obtain confidence interval — bootstrap_HR","text":"","code":"# Unanchored example load(system.file(\"extdata\", \"ipd.rda\", package = \"maicplus\", mustWork = TRUE)) load(system.file(\"extdata\", \"agd.rda\", package = \"maicplus\", mustWork = TRUE)) ipd_centered <- center_ipd(ipd = ipd, agd = agd)  pseudo_ipd <- read.csv(   system.file(\"extdata\", \"psuedo_IPD.csv\", package = \"maicplus\", mustWork = TRUE) ) pseudo_ipd$ARM <- \"B\" # Need to specify ARM for pseudo ipd  # Need to ensure pseudo_ipd and ipd_matched have same names colnames(pseudo_ipd) <- c(\"TIME\", \"EVENT\", \"ARM\") if (FALSE) { HR_bootstraps <- boot(   data = ipd_centered,   statistic = bootstrap_HR,   centered_colnames = centered_colnames,   pseudo_ipd = pseudo_ipd,   model = Surv(TIME, EVENT == 1) ~ ARM,   ref_treat = \"B\",   R = 1000 ) }"},{"path":"https://hta-pharma.github.io/maicplus/reference/bucher.html","id":null,"dir":"Reference","previous_headings":"","what":"Bucher method for combining treatment effects — bucher","title":"Bucher method for combining treatment effects — bucher","text":"Given two treatment effects vs. C B vs. C derive treatment effects vs. B using Bucher method. Two-sided confidence interval Z-test p-value also calculated. Treatment effects standard errors log scale hazard ratio, odds ratio, risk ratio. Treatment effects standard errors natural scale risk difference mean difference.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/bucher.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bucher method for combining treatment effects — bucher","text":"","code":"bucher(trt, com, conf_lv = 0.95)  # S3 method for maicplus_bucher print(x, ci_digits = 2, pval_digits = 3, exponentiate = FALSE, ...)"},{"path":"https://hta-pharma.github.io/maicplus/reference/bucher.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bucher method for combining treatment effects — bucher","text":"trt list two scalars study experimental arm. 'est' point estimate 'se' standard error treatment effect. time--event data, 'est' 'se' point estimate standard error log hazard ratio. binary data, 'est' 'se' point estimate standard error log odds ratio, log risk ratio, risk difference. continuous data,  'est' 'se' point estimate standard error mean difference. com trt, study control arm conf_lv numerical scalar, prescribe confidence level derive two-sided confidence interval treatment effect x maicplus_bucher object ci_digits integer, number decimal places point estimate derived confidence limits pval_digits integer, number decimal places display Z-test p-value exponentiate whether treatment effect confidence interval exponentiated. applies relative treatment effects. Default set false. ... used","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/bucher.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bucher method for combining treatment effects — bucher","text":"list 5 elements, est scalar, point estimate treatment effect se scalar, standard error treatment effect ci_l scalar, lower confidence limit two-sided CI prescribed nominal level conf_lv ci_u scalar, upper confidence limit two-sided CI prescribed nominal level conf_lv pval p-value Z-test, null hypothesis est zero","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/bucher.html","id":"methods-by-generic-","dir":"Reference","previous_headings":"","what":"Methods (by generic)","title":"Bucher method for combining treatment effects — bucher","text":"print(maicplus_bucher): Print method maicplus_bucher objects","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/bucher.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bucher method for combining treatment effects — bucher","text":"","code":"trt <- list(est = log(1.1), se = 0.2) com <- list(est = log(1.3), se = 0.18) result <- bucher(trt, com, conf_lv = 0.9) print(result, ci_digits = 3, pval_digits = 3) #>                   result                   pvalue  #> \"-0.167 [-0.610; 0.276]\"                  \"0.535\""},{"path":"https://hta-pharma.github.io/maicplus/reference/calculate_weights_legend.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","title":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","text":"Calculates ESS reduction median weights used create legend weights plot","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/calculate_weights_legend.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","text":"","code":"calculate_weights_legend(weighted_data)"},{"path":"https://hta-pharma.github.io/maicplus/reference/calculate_weights_legend.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","text":"weighted_data object returned calculating weights using estimate_weights","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/calculate_weights_legend.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","text":"list ESS, ESS reduction, median value scaled unscaled weights, missing count","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/calculate_weights_legend.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Statistics for Weight Plot Legend — calculate_weights_legend","text":"","code":"data(\"weighted_sat\") calculate_weights_legend(weighted_sat) #> $ess #> [1] 121.99 #>  #> $ess_reduction #> [1] 75.6 #>  #> $wt_median #> [1] 0.0567 #>  #> $wt_scaled_median #> [1] 0.1635 #>  #> $nr_na #> [1] 0 #>"},{"path":"https://hta-pharma.github.io/maicplus/reference/center_ipd.html","id":null,"dir":"Reference","previous_headings":"","what":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","title":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","text":"function subtracts IPD variables (prognostic variables /effect modifiers) aggregate data averages. centering needed order calculate weights. IPD aggregate data variable names match.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/center_ipd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","text":"","code":"center_ipd(ipd, agd)"},{"path":"https://hta-pharma.github.io/maicplus/reference/center_ipd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","text":"ipd IPD variable names match aggregate data names without suffix. involve either changing aggregate data name ipd name. instance, binarize SEX variable MALE reference using dummize_ipd, function names new variable SEX_MALE. case, SEX_MALE also available aggregate data. agd pre-processed aggregate data contain STUDY, ARM, N. Variable names followed legal suffixes (.e. MEAN, MEDIAN, SD, PROP). Note COUNT suffix longer accepted.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/center_ipd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","text":"centered ipd using aggregate level data averages","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/center_ipd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Center individual patient data (IPD) variables using aggregate data averages — center_ipd","text":"","code":"# load in IPD data(adsl_sat) adsl <- dummize_ipd(adsl_sat, dummize_cols = c(\"SEX\"), dummize_ref_level = c(\"Female\"))  # Reading aggregate data by Excel target_pop <- read.csv(   system.file(\"extdata\", \"aggregate_data_example_1.csv\", package = \"maicplus\", mustWork = TRUE) ) agd <- process_agd(target_pop)  # Alternatively, you can specify aggregate data manually in data frame data(agd) ipd_centered <- center_ipd(ipd = adsl, agd = agd)"},{"path":"https://hta-pharma.github.io/maicplus/reference/centered_ipd_sat.html","id":null,"dir":"Reference","previous_headings":"","what":"Centered patient data from single arm trial — centered_ipd_sat","title":"Centered patient data from single arm trial — centered_ipd_sat","text":"Centered patient data single arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/centered_ipd_sat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Centered patient data from single arm trial — centered_ipd_sat","text":"","code":"centered_ipd_sat"},{"path":"https://hta-pharma.github.io/maicplus/reference/centered_ipd_sat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Centered patient data from single arm trial — centered_ipd_sat","text":"data frame 500 rows 14 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm. AGE Age years baseline. SEX Sex patient recorded character \"Male\"/\"Female\". SMOKE Smoking status baseline integer 1/0. ECOG0 Indicator ECOG score = 0 baseline integer 1/0. N_PR_THER Number prior therapies received integer 1, 2, 3, 4. SEX_MALE Indicator SEX == \"Male\" numeric 1/0. AGE_CENTERED Age years baseline relative average aggregate data agd. AGE_MEDIAN_CENTERED AGE greater/less MEDIAN_AGE agd coded 1/0 centered 0.5. AGE_SQUARED_CENTERED AGE squared centered respect AGE agd. squared age aggregate data derived \\(E(X^2)\\) term variance formula. SEX_MALE_CENTERED SEX_MALE centered proportion male patients agd ECOG0_CENTERED ECOG0 centered proportion ECOG0 agd SMOKE_CENTERED SMOKE centered proportion SMOKE agd N_PR_THER_MEDIAN_CENTERED N_PR_THER centered median agd.","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/reference/centered_ipd_twt.html","id":null,"dir":"Reference","previous_headings":"","what":"Centered patient data from two arm trial — centered_ipd_twt","title":"Centered patient data from two arm trial — centered_ipd_twt","text":"Centered patient data two arm trial","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/centered_ipd_twt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Centered patient data from two arm trial — centered_ipd_twt","text":"","code":"centered_ipd_twt"},{"path":"https://hta-pharma.github.io/maicplus/reference/centered_ipd_twt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Centered patient data from two arm trial — centered_ipd_twt","text":"data frame 1000 rows 14 columns: USUBJID Unique subject identifiers patients. ARM Assigned treatment arm. AGE Age years baseline. SEX Sex patient recorded character \"Male\"/\"Female\". SMOKE Smoking status baseline integer 1/0. ECOG0 Indicator ECOG score = 0 baseline integer 1/0. N_PR_THER Number prior therapies received integer 1, 2, 3, 4. SEX_MALE Indicator SEX == \"Male\" numeric 1/0. AGE_CENTERED Age years baseline relative average aggregate data agd. AGE_MEDIAN_CENTERED AGE greater/less MEDIAN_AGE agd coded 1/0 centered 0.5. AGE_SQUARED_CENTERED AGE squared centered respect AGE agd. squared age aggregate data derived \\(E(X^2)\\) term variance formula. SEX_MALE_CENTERED SEX_MALE centered proportion male patients agd ECOG0_CENTERED ECOG0 centered proportion ECOG0 agd SMOKE_CENTERED SMOKE centered proportion SMOKE agd N_PR_THER_MEDIAN_CENTERED N_PR_THER centered median agd.","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/reference/check_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Check to see if weights are optimized correctly — check_weights","title":"Check to see if weights are optimized correctly — check_weights","text":"function checks see optimization done properly checking covariate averages adjustment.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/check_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check to see if weights are optimized correctly — check_weights","text":"","code":"check_weights(weighted_data, processed_agd)  # S3 method for maicplus_check_weights print(   x,   mean_digits = 2,   prop_digits = 2,   sd_digits = 3,   digits = getOption(\"digits\"),   ... )"},{"path":"https://hta-pharma.github.io/maicplus/reference/check_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check to see if weights are optimized correctly — check_weights","text":"weighted_data object returned calculating weights using estimate_weights processed_agd data frame, object returned using process_agd aggregated data following naming convention x object check_weights mean_digits number digits rounding mean columns output prop_digits number digits rounding proportion columns output sd_digits number digits rounding mean columns output digits minimal number significant digits, see print.default. ... arguments print.data.frame","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/check_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check to see if weights are optimized correctly — check_weights","text":"data.frame weighted unweighted covariate averages IPD, average aggregate data, sum inner products covariate \\(x_i\\) weights (\\(exp(x_i\\beta)\\))","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/check_weights.html","id":"methods-by-generic-","dir":"Reference","previous_headings":"","what":"Methods (by generic)","title":"Check to see if weights are optimized correctly — check_weights","text":"print(maicplus_check_weights): Print method check_weights objects","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/check_weights.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check to see if weights are optimized correctly — check_weights","text":"","code":"check_weights(weighted_sat, process_agd(agd)) #>   covariate match_stat internal_trial internal_trial_after_weighted #> 1       AGE       Mean         59.850                         51.00 #> 2       AGE     Median         59.000                         49.00 #> 3       AGE         SD          9.011                          3.25 #> 4  SEX_MALE       Prop          0.380                          0.49 #> 5     ECOG0       Prop          0.410                          0.35 #> 6     SMOKE       Prop          0.320                          0.19 #>   external_trial sum_centered_IPD_with_weights #> 1          51.00                         0.000 #> 2          49.00                         0.000 #> 3           3.25                        -0.004 #> 4           0.49                         0.000 #> 5           0.35                         0.000 #> 6           0.19                         0.000"},{"path":"https://hta-pharma.github.io/maicplus/reference/complete_agd.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate pooled arm statistics in Aggregated Data (AgD) based on arm-specific statistics — complete_agd","title":"Calculate pooled arm statistics in Aggregated Data (AgD) based on arm-specific statistics — complete_agd","text":"convenient function pool arm statistics. function called within process_agd ARM equal \"Total\". Note pooled median calculated approximation.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/complete_agd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate pooled arm statistics in Aggregated Data (AgD) based on arm-specific statistics — complete_agd","text":"","code":"complete_agd(use_agd)"},{"path":"https://hta-pharma.github.io/maicplus/reference/complete_agd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate pooled arm statistics in Aggregated Data (AgD) based on arm-specific statistics — complete_agd","text":"use_agd aggregated data processed within process_agd","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/complete_agd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate pooled arm statistics in Aggregated Data (AgD) based on arm-specific statistics — complete_agd","text":"Complete N, count, mean, sd, median pooled arm","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/dummize_ipd.html","id":null,"dir":"Reference","previous_headings":"","what":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","title":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","text":"convenient function convert categorical variables dummy binary variables. especially useful variable two factors. Note original variable kept variable dummized.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/dummize_ipd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","text":"","code":"dummize_ipd(raw_ipd, dummize_cols, dummize_ref_level)"},{"path":"https://hta-pharma.github.io/maicplus/reference/dummize_ipd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","text":"raw_ipd ipd data contains variable dummize dummize_cols vector column names binarize dummize_ref_level vector reference level variables binarize","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/dummize_ipd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","text":"ipd dummized columns","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/dummize_ipd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create dummy variables from categorical variables in an individual patient data (ipd) — dummize_ipd","text":"","code":"data(adsl_twt) dummize_ipd(adsl_twt, dummize_cols = c(\"SEX\"), dummize_ref_level = c(\"Male\")) #>      USUBJID ARM AGE    SEX SMOKE ECOG0 N_PR_THER SEX_MALE SEX_FEMALE #> 1        xx1   A  45   Male     0     0         4        1          0 #> 2        xx2   A  71   Male     0     0         3        1          0 #> 3        xx3   A  58   Male     1     1         2        1          0 #> 4        xx4   A  48 Female     0     1         4        0          1 #> 5        xx5   A  69   Male     0     1         4        1          0 #> 6        xx6   A  48 Female     0     1         4        0          1 #> 7        xx7   A  47   Male     1     0         3        1          0 #> 8        xx8   A  61   Male     1     0         1        1          0 #> 9        xx9   A  54 Female     1     1         1        0          1 #> 10      xx10   A  56 Female     1     0         3        0          1 #> 11      xx11   A  63 Female     0     0         4        0          1 #> 12      xx12   A  50 Female     0     0         1        0          1 #> 13      xx13   A  57   Male     0     1         3        1          0 #> 14      xx14   A  62 Female     1     1         1        0          1 #> 15      xx15   A  57 Female     0     1         3        0          1 #> 16      xx16   A  66   Male     0     0         2        1          0 #> 17      xx17   A  75   Male     1     1         3        1          0 #> 18      xx18   A  47 Female     0     0         4        0          1 #> 19      xx19   A  57   Male     0     0         3        1          0 #> 20      xx20   A  54   Male     0     0         3        1          0 #> 21      xx21   A  55   Male     1     0         3        1          0 #> 22      xx22   A  64   Male     0     1         3        1          0 #> 23      xx23   A  53 Female     1     0         3        0          1 #> 24      xx24   A  58   Male     1     1         2        1          0 #> 25      xx25   A  47   Male     0     0         1        1          0 #> 26      xx26   A  60 Female     1     0         1        0          1 #> 27      xx27   A  49 Female     0     1         3        0          1 #> 28      xx28   A  55 Female     0     0         1        0          1 #> 29      xx29   A  66 Female     0     1         2        0          1 #> 30      xx30   A  58   Male     0     1         4        1          0 #> 31      xx31   A  49   Male     0     1         4        1          0 #> 32      xx32   A  61   Male     0     0         4        1          0 #> 33      xx33   A  66   Male     1     0         3        1          0 #> 34      xx34   A  45   Male     0     0         1        1          0 #> 35      xx35   A  59 Female     1     1         2        0          1 #> 36      xx36   A  74 Female     1     0         4        0          1 #> 37      xx37   A  73 Female     0     0         3        0          1 #> 38      xx38   A  74   Male     0     1         4        1          0 #> 39      xx39   A  54   Male     0     0         1        1          0 #> 40      xx40   A  58 Female     1     1         1        0          1 #> 41      xx41   A  61 Female     0     1         3        0          1 #> 42      xx42   A  47 Female     1     1         2        0          1 #> 43      xx43   A  73 Female     1     1         2        0          1 #> 44      xx44   A  68   Male     0     0         1        1          0 #> 45      xx45   A  49 Female     0     0         3        0          1 #> 46      xx46   A  71 Female     0     0         2        0          1 #> 47      xx47   A  70   Male     0     1         4        1          0 #> 48      xx48   A  62 Female     1     0         1        0          1 #> 49      xx49   A  49   Male     0     0         1        1          0 #> 50      xx50   A  74 Female     0     0         1        0          1 #> 51      xx51   A  46 Female     0     1         3        0          1 #> 52      xx52   A  68 Female     1     0         3        0          1 #> 53      xx53   A  46   Male     1     0         2        1          0 #> 54      xx54   A  75 Female     1     1         3        0          1 #> 55      xx55   A  47 Female     0     0         3        0          1 #> 56      xx56   A  56   Male     0     1         3        1          0 #> 57      xx57   A  72 Female     0     0         3        0          1 #> 58      xx58   A  57   Male     1     1         4        1          0 #> 59      xx59   A  46   Male     0     0         1        1          0 #> 60      xx60   A  56 Female     1     1         1        0          1 #> 61      xx61   A  73   Male     0     1         2        1          0 #> 62      xx62   A  60 Female     1     1         3        0          1 #> 63      xx63   A  75   Male     0     0         2        1          0 #> 64      xx64   A  69 Female     1     1         2        0          1 #> 65      xx65   A  47 Female     0     1         1        0          1 #> 66      xx66   A  74   Male     0     0         4        1          0 #> 67      xx67   A  71 Female     0     1         1        0          1 #> 68      xx68   A  49 Female     1     1         1        0          1 #> 69      xx69   A  68   Male     0     0         3        1          0 #> 70      xx70   A  49   Male     0     1         1        1          0 #> 71      xx71   A  70   Male     0     1         1        1          0 #> 72      xx72   A  45 Female     0     0         2        0          1 #> 73      xx73   A  47 Female     0     1         3        0          1 #> 74      xx74   A  58   Male     0     1         3        1          0 #> 75      xx75   A  49 Female     0     1         4        0          1 #> 76      xx76   A  68 Female     0     0         1        0          1 #> 77      xx77   A  60   Male     0     0         4        1          0 #> 78      xx78   A  45 Female     1     0         1        0          1 #> 79      xx79   A  57 Female     0     0         1        0          1 #> 80      xx80   A  50 Female     0     1         1        0          1 #> 81      xx81   A  63   Male     0     1         3        1          0 #> 82      xx82   A  47 Female     0     0         2        0          1 #> 83      xx83   A  68 Female     0     1         4        0          1 #> 84      xx84   A  51   Male     0     0         4        1          0 #> 85      xx85   A  60   Male     0     0         1        1          0 #> 86      xx86   A  52 Female     1     0         4        0          1 #> 87      xx87   A  69   Male     1     1         1        1          0 #> 88      xx88   A  70 Female     0     1         4        0          1 #> 89      xx89   A  72   Male     0     0         2        1          0 #> 90      xx90   A  46 Female     0     1         1        0          1 #> 91      xx91   A  51   Male     1     0         4        1          0 #> 92      xx92   A  69 Female     0     1         1        0          1 #> 93      xx93   A  66   Male     0     0         1        1          0 #> 94      xx94   A  73   Male     0     1         1        1          0 #> 95      xx95   A  73 Female     1     0         3        0          1 #> 96      xx96   A  62 Female     0     1         2        0          1 #> 97      xx97   A  55 Female     0     0         4        0          1 #> 98      xx98   A  67   Male     1     0         3        1          0 #> 99      xx99   A  54 Female     1     0         3        0          1 #> 100    xx100   A  52 Female     1     1         4        0          1 #> 101    xx101   A  57   Male     0     0         2        1          0 #> 102    xx102   A  57 Female     1     1         3        0          1 #> 103    xx103   A  57   Male     0     0         3        1          0 #> 104    xx104   A  67 Female     1     1         2        0          1 #> 105    xx105   A  67 Female     1     1         2        0          1 #> 106    xx106   A  74 Female     1     1         2        0          1 #> 107    xx107   A  72 Female     1     0         2        0          1 #> 108    xx108   A  73 Female     0     0         3        0          1 #> 109    xx109   A  57 Female     0     0         4        0          1 #> 110    xx110   A  69 Female     1     0         1        0          1 #> 111    xx111   A  55   Male     0     0         1        1          0 #> 112    xx112   A  74 Female     0     0         4        0          1 #> 113    xx113   A  68 Female     0     0         4        0          1 #> 114    xx114   A  53   Male     0     0         2        1          0 #> 115    xx115   A  69   Male     0     0         2        1          0 #> 116    xx116   A  68   Male     0     1         2        1          0 #> 117    xx117   A  58   Male     0     0         1        1          0 #> 118    xx118   A  64 Female     0     0         3        0          1 #> 119    xx119   A  71   Male     0     0         1        1          0 #> 120    xx120   A  69 Female     0     1         2        0          1 #> 121    xx121   A  64 Female     1     0         4        0          1 #> 122    xx122   A  64   Male     1     0         1        1          0 #> 123    xx123   A  55   Male     0     1         3        1          0 #> 124    xx124   A  74   Male     0     0         3        1          0 #> 125    xx125   A  50   Male     0     1         3        1          0 #> 126    xx126   A  68   Male     0     1         1        1          0 #> 127    xx127   A  60   Male     0     0         2        1          0 #> 128    xx128   A  59 Female     0     0         3        0          1 #> 129    xx129   A  71 Female     1     0         3        0          1 #> 130    xx130   A  69   Male     0     1         4        1          0 #> 131    xx131   A  56 Female     0     1         1        0          1 #> 132    xx132   A  51   Male     0     1         3        1          0 #> 133    xx133   A  65   Male     0     1         2        1          0 #> 134    xx134   A  45   Male     1     1         3        1          0 #> 135    xx135   A  49 Female     0     0         2        0          1 #> 136    xx136   A  74 Female     1     0         4        0          1 #> 137    xx137   A  71 Female     1     1         3        0          1 #> 138    xx138   A  65   Male     0     0         1        1          0 #> 139    xx139   A  67 Female     0     0         2        0          1 #> 140    xx140   A  48 Female     0     0         3        0          1 #> 141    xx141   A  70 Female     0     1         4        0          1 #> 142    xx142   A  72 Female     0     0         1        0          1 #> 143    xx143   A  53   Male     1     1         4        1          0 #> 144    xx144   A  68 Female     1     0         3        0          1 #> 145    xx145   A  65   Male     0     1         1        1          0 #> 146    xx146   A  70 Female     1     1         4        0          1 #> 147    xx147   A  58   Male     0     0         2        1          0 #> 148    xx148   A  68 Female     0     1         4        0          1 #> 149    xx149   A  54 Female     0     0         4        0          1 #> 150    xx150   A  75 Female     1     1         4        0          1 #> 151    xx151   A  49 Female     1     1         3        0          1 #> 152    xx152   A  60   Male     0     0         4        1          0 #> 153    xx153   A  45 Female     1     0         4        0          1 #> 154    xx154   A  73 Female     1     0         3        0          1 #> 155    xx155   A  71 Female     0     1         3        0          1 #> 156    xx156   A  73 Female     0     0         3        0          1 #> 157    xx157   A  58 Female     0     0         4        0          1 #> 158    xx158   A  59 Female     0     1         2        0          1 #> 159    xx159   A  75 Female     0     1         2        0          1 #> 160    xx160   A  65   Male     0     0         2        1          0 #> 161    xx161   A  48   Male     1     0         2        1          0 #> 162    xx162   A  63 Female     0     1         2        0          1 #> 163    xx163   A  74 Female     0     0         3        0          1 #> 164    xx164   A  64 Female     0     0         3        0          1 #> 165    xx165   A  49 Female     0     0         1        0          1 #> 166    xx166   A  56 Female     0     1         4        0          1 #> 167    xx167   A  56 Female     0     1         2        0          1 #> 168    xx168   A  48 Female     0     0         1        0          1 #> 169    xx169   A  65   Male     0     1         3        1          0 #> 170    xx170   A  53 Female     0     0         3        0          1 #> 171    xx171   A  72   Male     0     0         2        1          0 #> 172    xx172   A  75 Female     1     0         4        0          1 #> 173    xx173   A  70 Female     0     1         2        0          1 #> 174    xx174   A  53 Female     1     0         2        0          1 #> 175    xx175   A  45 Female     1     1         3        0          1 #> 176    xx176   A  53 Female     1     1         3        0          1 #> 177    xx177   A  52 Female     0     1         2        0          1 #> 178    xx178   A  61 Female     1     0         4        0          1 #> 179    xx179   A  70   Male     0     0         3        1          0 #> 180    xx180   A  58 Female     0     0         3        0          1 #> 181    xx181   A  54   Male     0     0         1        1          0 #> 182    xx182   A  53   Male     0     0         2        1          0 #> 183    xx183   A  74   Male     0     0         2        1          0 #> 184    xx184   A  64   Male     0     1         3        1          0 #> 185    xx185   A  52   Male     0     0         1        1          0 #> 186    xx186   A  73 Female     0     0         3        0          1 #> 187    xx187   A  55 Female     1     0         4        0          1 #> 188    xx188   A  71 Female     0     0         3        0          1 #> 189    xx189   A  57 Female     1     0         3        0          1 #> 190    xx190   A  49 Female     1     0         2        0          1 #> 191    xx191   A  69   Male     0     0         4        1          0 #> 192    xx192   A  74 Female     1     0         2        0          1 #> 193    xx193   A  59 Female     0     0         3        0          1 #> 194    xx194   A  53   Male     0     0         4        1          0 #> 195    xx195   A  52 Female     0     1         3        0          1 #> 196    xx196   A  47 Female     1     1         1        0          1 #> 197    xx197   A  61 Female     0     0         4        0          1 #> 198    xx198   A  51 Female     0     0         2        0          1 #> 199    xx199   A  62 Female     1     0         1        0          1 #> 200    xx200   A  59 Female     1     0         2        0          1 #> 201    xx201   A  58   Male     0     0         2        1          0 #> 202    xx202   A  61 Female     0     1         4        0          1 #> 203    xx203   A  45 Female     0     0         1        0          1 #> 204    xx204   A  59   Male     1     1         2        1          0 #> 205    xx205   A  58 Female     1     0         2        0          1 #> 206    xx206   A  67   Male     0     0         1        1          0 #> 207    xx207   A  51 Female     1     0         2        0          1 #> 208    xx208   A  68   Male     1     1         3        1          0 #> 209    xx209   A  53 Female     1     0         2        0          1 #> 210    xx210   A  64   Male     1     0         3        1          0 #> 211    xx211   A  61   Male     0     1         1        1          0 #> 212    xx212   A  52   Male     1     0         2        1          0 #> 213    xx213   A  57 Female     0     1         4        0          1 #> 214    xx214   A  57 Female     1     0         2        0          1 #> 215    xx215   A  49 Female     0     0         4        0          1 #> 216    xx216   A  51   Male     1     1         3        1          0 #> 217    xx217   A  60 Female     0     0         2        0          1 #> 218    xx218   A  60 Female     1     0         1        0          1 #> 219    xx219   A  71 Female     0     0         3        0          1 #> 220    xx220   A  54 Female     0     0         1        0          1 #> 221    xx221   A  51   Male     1     0         1        1          0 #> 222    xx222   A  71   Male     0     1         3        1          0 #> 223    xx223   A  47   Male     0     0         2        1          0 #> 224    xx224   A  65   Male     0     0         1        1          0 #> 225    xx225   A  53 Female     0     0         1        0          1 #> 226    xx226   A  56 Female     0     0         1        0          1 #> 227    xx227   A  51   Male     1     0         4        1          0 #> 228    xx228   A  68 Female     0     0         2        0          1 #> 229    xx229   A  75 Female     0     1         1        0          1 #> 230    xx230   A  49 Female     0     0         2        0          1 #> 231    xx231   A  74   Male     1     0         1        1          0 #> 232    xx232   A  66 Female     0     0         1        0          1 #> 233    xx233   A  74 Female     0     0         1        0          1 #> 234    xx234   A  62 Female     0     1         2        0          1 #> 235    xx235   A  53 Female     0     0         1        0          1 #> 236    xx236   A  62 Female     0     1         1        0          1 #> 237    xx237   A  70 Female     1     0         3        0          1 #> 238    xx238   A  60 Female     1     0         2        0          1 #> 239    xx239   A  72   Male     0     0         1        1          0 #> 240    xx240   A  74 Female     1     0         3        0          1 #> 241    xx241   A  47 Female     1     0         3        0          1 #> 242    xx242   A  54 Female     0     1         3        0          1 #> 243    xx243   A  65 Female     0     1         2        0          1 #> 244    xx244   A  74   Male     0     1         1        1          0 #> 245    xx245   A  61   Male     0     1         2        1          0 #> 246    xx246   A  54 Female     1     0         3        0          1 #> 247    xx247   A  65   Male     0     0         3        1          0 #> 248    xx248   A  72 Female     1     0         3        0          1 #> 249    xx249   A  71 Female     1     1         2        0          1 #> 250    xx250   A  65   Male     0     0         3        1          0 #> 251    xx251   A  58   Male     0     1         1        1          0 #> 252    xx252   A  46   Male     0     0         1        1          0 #> 253    xx253   A  53 Female     1     0         4        0          1 #> 254    xx254   A  71 Female     1     0         4        0          1 #> 255    xx255   A  47   Male     0     1         3        1          0 #> 256    xx256   A  45   Male     0     0         1        1          0 #> 257    xx257   A  50   Male     0     0         3        1          0 #> 258    xx258   A  67 Female     1     0         3        0          1 #> 259    xx259   A  72   Male     1     0         2        1          0 #> 260    xx260   A  45 Female     0     1         2        0          1 #> 261    xx261   A  75 Female     1     0         1        0          1 #> 262    xx262   A  65   Male     0     0         3        1          0 #> 263    xx263   A  60 Female     0     1         3        0          1 #> 264    xx264   A  75 Female     0     1         4        0          1 #> 265    xx265   A  60 Female     1     0         4        0          1 #> 266    xx266   A  49 Female     1     0         2        0          1 #> 267    xx267   A  58 Female     0     1         1        0          1 #> 268    xx268   A  57   Male     1     1         3        1          0 #> 269    xx269   A  69   Male     1     0         2        1          0 #> 270    xx270   A  51   Male     0     1         1        1          0 #> 271    xx271   A  54 Female     1     0         4        0          1 #> 272    xx272   A  55   Male     0     1         3        1          0 #> 273    xx273   A  49 Female     0     0         4        0          1 #> 274    xx274   A  74 Female     1     0         1        0          1 #> 275    xx275   A  55   Male     1     0         1        1          0 #> 276    xx276   A  52 Female     1     0         1        0          1 #> 277    xx277   A  65   Male     0     0         2        1          0 #> 278    xx278   A  70 Female     1     0         1        0          1 #> 279    xx279   A  66 Female     1     1         2        0          1 #> 280    xx280   A  63 Female     0     1         4        0          1 #> 281    xx281   A  61 Female     0     1         3        0          1 #> 282    xx282   A  65   Male     0     1         2        1          0 #> 283    xx283   A  73   Male     0     0         2        1          0 #> 284    xx284   A  55 Female     1     1         4        0          1 #> 285    xx285   A  56 Female     1     1         4        0          1 #> 286    xx286   A  68 Female     0     1         1        0          1 #> 287    xx287   A  74 Female     1     0         4        0          1 #> 288    xx288   A  67 Female     0     0         2        0          1 #> 289    xx289   A  66   Male     0     1         3        1          0 #> 290    xx290   A  48 Female     0     0         3        0          1 #> 291    xx291   A  49 Female     1     1         3        0          1 #> 292    xx292   A  60 Female     1     1         1        0          1 #> 293    xx293   A  69 Female     0     1         4        0          1 #> 294    xx294   A  58 Female     0     1         3        0          1 #> 295    xx295   A  45 Female     1     1         4        0          1 #> 296    xx296   A  49 Female     0     1         1        0          1 #> 297    xx297   A  67 Female     1     0         4        0          1 #> 298    xx298   A  63   Male     0     0         4        1          0 #> 299    xx299   A  50 Female     0     0         2        0          1 #> 300    xx300   A  68 Female     0     1         3        0          1 #> 301    xx301   A  53   Male     0     1         2        1          0 #> 302    xx302   A  63   Male     0     1         2        1          0 #> 303    xx303   A  58   Male     0     0         4        1          0 #> 304    xx304   A  70 Female     0     1         4        0          1 #> 305    xx305   A  56 Female     0     0         1        0          1 #> 306    xx306   A  56   Male     0     1         3        1          0 #> 307    xx307   A  61 Female     0     0         3        0          1 #> 308    xx308   A  72   Male     0     0         2        1          0 #> 309    xx309   A  51   Male     0     1         1        1          0 #> 310    xx310   A  72   Male     0     0         4        1          0 #> 311    xx311   A  64 Female     0     0         3        0          1 #> 312    xx312   A  59   Male     0     0         2        1          0 #> 313    xx313   A  75 Female     0     0         3        0          1 #> 314    xx314   A  75 Female     0     0         1        0          1 #> 315    xx315   A  74   Male     0     0         2        1          0 #> 316    xx316   A  54   Male     0     1         4        1          0 #> 317    xx317   A  55 Female     0     0         3        0          1 #> 318    xx318   A  52 Female     1     0         1        0          1 #> 319    xx319   A  46 Female     0     1         1        0          1 #> 320    xx320   A  53   Male     0     0         1        1          0 #> 321    xx321   A  54 Female     0     1         1        0          1 #> 322    xx322   A  62 Female     0     0         2        0          1 #> 323    xx323   A  54   Male     1     0         4        1          0 #> 324    xx324   A  56 Female     0     0         4        0          1 #> 325    xx325   A  48 Female     0     0         1        0          1 #> 326    xx326   A  52 Female     0     1         1        0          1 #> 327    xx327   A  55 Female     0     1         3        0          1 #> 328    xx328   A  69 Female     0     0         2        0          1 #> 329    xx329   A  48 Female     0     0         1        0          1 #> 330    xx330   A  48 Female     1     1         3        0          1 #> 331    xx331   A  60   Male     0     0         3        1          0 #> 332    xx332   A  74 Female     0     1         2        0          1 #> 333    xx333   A  45 Female     0     0         1        0          1 #> 334    xx334   A  64   Male     1     0         1        1          0 #> 335    xx335   A  75 Female     1     1         3        0          1 #> 336    xx336   A  62 Female     0     0         3        0          1 #> 337    xx337   A  71   Male     0     0         4        1          0 #> 338    xx338   A  48 Female     1     1         3        0          1 #> 339    xx339   A  53 Female     0     0         2        0          1 #> 340    xx340   A  62   Male     0     0         3        1          0 #> 341    xx341   A  69 Female     0     0         2        0          1 #> 342    xx342   A  72 Female     0     0         1        0          1 #> 343    xx343   A  61 Female     0     0         2        0          1 #> 344    xx344   A  47   Male     0     0         2        1          0 #> 345    xx345   A  58   Male     0     1         1        1          0 #> 346    xx346   A  52 Female     0     0         4        0          1 #> 347    xx347   A  49 Female     0     0         4        0          1 #> 348    xx348   A  51 Female     0     0         1        0          1 #> 349    xx349   A  51 Female     1     1         2        0          1 #> 350    xx350   A  72   Male     0     0         3        1          0 #> 351    xx351   A  68   Male     0     0         3        1          0 #> 352    xx352   A  49 Female     0     0         4        0          1 #> 353    xx353   A  45 Female     0     0         3        0          1 #> 354    xx354   A  49 Female     0     0         3        0          1 #> 355    xx355   A  65   Male     0     1         2        1          0 #> 356    xx356   A  56   Male     0     0         2        1          0 #> 357    xx357   A  45 Female     1     1         1        0          1 #> 358    xx358   A  57   Male     1     0         2        1          0 #> 359    xx359   A  53   Male     1     1         2        1          0 #> 360    xx360   A  65 Female     0     0         2        0          1 #> 361    xx361   A  57   Male     0     1         4        1          0 #> 362    xx362   A  55 Female     0     1         4        0          1 #> 363    xx363   A  57   Male     0     1         2        1          0 #> 364    xx364   A  46 Female     0     1         4        0          1 #> 365    xx365   A  69 Female     0     1         1        0          1 #> 366    xx366   A  67 Female     0     1         3        0          1 #> 367    xx367   A  55   Male     0     0         1        1          0 #> 368    xx368   A  53 Female     0     1         3        0          1 #> 369    xx369   A  46 Female     0     0         3        0          1 #> 370    xx370   A  71   Male     0     0         4        1          0 #> 371    xx371   A  68   Male     0     1         1        1          0 #> 372    xx372   A  49 Female     0     1         3        0          1 #> 373    xx373   A  51 Female     0     0         3        0          1 #> 374    xx374   A  65 Female     1     1         3        0          1 #> 375    xx375   A  55 Female     0     0         4        0          1 #> 376    xx376   A  53   Male     0     0         4        1          0 #> 377    xx377   A  65 Female     0     1         4        0          1 #> 378    xx378   A  72 Female     1     1         4        0          1 #> 379    xx379   A  61   Male     0     1         1        1          0 #> 380    xx380   A  73 Female     0     0         1        0          1 #> 381    xx381   A  62 Female     1     1         2        0          1 #> 382    xx382   A  46   Male     0     0         2        1          0 #> 383    xx383   A  51   Male     0     1         4        1          0 #> 384    xx384   A  60   Male     1     1         4        1          0 #> 385    xx385   A  56 Female     0     1         3        0          1 #> 386    xx386   A  69 Female     0     1         1        0          1 #> 387    xx387   A  58 Female     1     1         2        0          1 #> 388    xx388   A  58 Female     1     1         3        0          1 #> 389    xx389   A  53 Female     0     1         2        0          1 #> 390    xx390   A  47 Female     0     0         2        0          1 #> 391    xx391   A  59   Male     0     1         2        1          0 #> 392    xx392   A  47 Female     0     0         4        0          1 #> 393    xx393   A  60 Female     1     0         4        0          1 #> 394    xx394   A  73 Female     0     0         4        0          1 #> 395    xx395   A  60   Male     0     0         1        1          0 #> 396    xx396   A  75   Male     0     0         4        1          0 #> 397    xx397   A  65 Female     0     0         3        0          1 #> 398    xx398   A  68   Male     0     0         1        1          0 #> 399    xx399   A  55 Female     0     0         4        0          1 #> 400    xx400   A  46 Female     1     0         4        0          1 #> 401    xx401   A  45 Female     0     1         1        0          1 #> 402    xx402   A  70   Male     1     1         3        1          0 #> 403    xx403   A  56 Female     0     1         4        0          1 #> 404    xx404   A  62 Female     0     0         3        0          1 #> 405    xx405   A  49   Male     0     0         4        1          0 #> 406    xx406   A  52 Female     0     0         4        0          1 #> 407    xx407   A  67 Female     0     1         4        0          1 #> 408    xx408   A  50 Female     1     1         1        0          1 #> 409    xx409   A  68 Female     1     1         2        0          1 #> 410    xx410   A  54 Female     0     0         4        0          1 #> 411    xx411   A  65   Male     0     0         4        1          0 #> 412    xx412   A  55 Female     0     1         2        0          1 #> 413    xx413   A  53 Female     1     1         4        0          1 #> 414    xx414   A  71 Female     0     0         2        0          1 #> 415    xx415   A  48   Male     0     0         1        1          0 #> 416    xx416   A  54 Female     0     1         3        0          1 #> 417    xx417   A  75   Male     1     0         3        1          0 #> 418    xx418   A  53 Female     0     0         2        0          1 #> 419    xx419   A  50 Female     1     0         4        0          1 #> 420    xx420   A  64 Female     1     0         1        0          1 #> 421    xx421   A  65 Female     0     0         2        0          1 #> 422    xx422   A  65 Female     0     1         4        0          1 #> 423    xx423   A  60 Female     1     0         2        0          1 #> 424    xx424   A  70 Female     1     0         3        0          1 #> 425    xx425   A  51 Female     0     0         2        0          1 #> 426    xx426   A  45 Female     0     1         1        0          1 #> 427    xx427   A  75 Female     1     0         2        0          1 #> 428    xx428   A  52 Female     1     0         1        0          1 #> 429    xx429   A  70   Male     0     0         4        1          0 #> 430    xx430   A  69 Female     1     1         3        0          1 #> 431    xx431   A  64 Female     0     0         2        0          1 #> 432    xx432   A  68 Female     1     0         1        0          1 #> 433    xx433   A  51   Male     1     0         1        1          0 #> 434    xx434   A  59 Female     0     1         2        0          1 #> 435    xx435   A  57 Female     0     0         1        0          1 #> 436    xx436   A  47   Male     0     0         2        1          0 #> 437    xx437   A  65   Male     0     1         1        1          0 #> 438    xx438   A  65   Male     0     1         1        1          0 #> 439    xx439   A  65   Male     0     0         2        1          0 #> 440    xx440   A  46   Male     0     0         2        1          0 #> 441    xx441   A  64 Female     0     0         3        0          1 #> 442    xx442   A  57 Female     0     1         4        0          1 #> 443    xx443   A  67 Female     0     1         3        0          1 #> 444    xx444   A  61 Female     1     0         3        0          1 #> 445    xx445   A  56   Male     0     0         4        1          0 #> 446    xx446   A  52   Male     0     0         3        1          0 #> 447    xx447   A  74 Female     1     1         3        0          1 #> 448    xx448   A  75   Male     0     1         3        1          0 #> 449    xx449   A  58   Male     0     1         3        1          0 #> 450    xx450   A  57 Female     0     1         4        0          1 #> 451    xx451   A  55 Female     0     0         1        0          1 #> 452    xx452   A  53 Female     1     0         1        0          1 #> 453    xx453   A  75   Male     1     0         4        1          0 #> 454    xx454   A  65 Female     0     1         3        0          1 #> 455    xx455   A  65 Female     0     0         4        0          1 #> 456    xx456   A  58   Male     0     0         4        1          0 #> 457    xx457   A  71 Female     0     0         3        0          1 #> 458    xx458   A  71   Male     0     0         4        1          0 #> 459    xx459   A  59   Male     1     0         4        1          0 #> 460    xx460   A  46   Male     1     0         4        1          0 #> 461    xx461   A  51 Female     0     0         4        0          1 #> 462    xx462   A  56 Female     0     1         4        0          1 #> 463    xx463   A  66 Female     0     0         1        0          1 #> 464    xx464   A  59 Female     1     0         2        0          1 #> 465    xx465   A  48 Female     1     0         2        0          1 #> 466    xx466   A  68 Female     0     0         4        0          1 #> 467    xx467   A  57 Female     0     0         4        0          1 #> 468    xx468   A  63   Male     0     0         4        1          0 #> 469    xx469   A  62   Male     0     1         3        1          0 #> 470    xx470   A  70 Female     0     0         3        0          1 #> 471    xx471   A  55 Female     0     1         2        0          1 #> 472    xx472   A  56   Male     1     0         3        1          0 #> 473    xx473   A  51   Male     1     0         1        1          0 #> 474    xx474   A  51 Female     0     0         4        0          1 #> 475    xx475   A  46 Female     1     1         3        0          1 #> 476    xx476   A  52   Male     0     0         4        1          0 #> 477    xx477   A  71   Male     0     1         4        1          0 #> 478    xx478   A  54   Male     1     0         3        1          0 #> 479    xx479   A  55   Male     0     1         2        1          0 #> 480    xx480   A  46 Female     0     0         3        0          1 #> 481    xx481   A  70 Female     0     0         1        0          1 #> 482    xx482   A  68 Female     0     1         3        0          1 #> 483    xx483   A  50   Male     0     1         1        1          0 #> 484    xx484   A  45 Female     0     0         2        0          1 #> 485    xx485   A  68   Male     0     0         1        1          0 #> 486    xx486   A  56   Male     0     1         2        1          0 #> 487    xx487   A  59   Male     0     1         3        1          0 #> 488    xx488   A  51   Male     1     1         1        1          0 #> 489    xx489   A  61 Female     1     0         3        0          1 #> 490    xx490   A  60 Female     0     1         3        0          1 #> 491    xx491   A  68 Female     1     0         2        0          1 #> 492    xx492   A  67   Male     1     1         4        1          0 #> 493    xx493   A  45   Male     1     1         2        1          0 #> 494    xx494   A  71 Female     0     1         2        0          1 #> 495    xx495   A  55   Male     0     1         3        1          0 #> 496    xx496   A  72 Female     0     0         4        0          1 #> 497    xx497   A  48 Female     1     0         3        0          1 #> 498    xx498   A  68 Female     1     0         1        0          1 #> 499    xx499   A  45 Female     0     0         1        0          1 #> 500    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         0 #> 3571 yyxx110   C  45 Female     1     1         1        0          1 #> 2551 yyxx111   C  47   Male     0     1         3        1          0 #> 3831 yyxx112   C  51   Male     0     1         4        1          0 #> 1121 yyxx113   C  74 Female     0     0         4        0          1 #> 4841 yyxx114   C  45 Female     0     0         2        0          1 #> 731  yyxx115   C  47 Female     0     1         3        0          1 #> 4771 yyxx116   C  71   Male     0     1         4        1          0 #> 4371 yyxx117   C  65   Male     0     1         1        1          0 #> 971  yyxx118   C  55 Female     0     0         4        0          1 #> 1451 yyxx119   C  65   Male     0     1         1        1          0 #> 2701  yyxx12   C  51   Male     0     1         1        1          0 #> 3471 yyxx120   C  49 Female     0     0         4        0          1 #> 3781 yyxx121   C  72 Female     1     1         4        0          1 #> 1010 yyxx122   C  56 Female     1     0         3        0          1 #> 1161 yyxx123   C  68   Male     0     1         2        1          0 #> 4201 yyxx124   C  64 Female     1     0         1        0          1 #> 3251 yyxx125   C  48 Female     0     0         1        0          1 #> 2111 yyxx126   C  61   Male     0     1         1        1          0 #> 3891 yyxx127   C  53 Female     0     1         2        0          1 #> 2261 yyxx128   C  56 Female     0     0         1        0          1 #> 1641 yyxx129   C  64 Female     0     0         3        0          1 #> 4361  yyxx13   C  47   Male     0     0         2        1          0 #> 1551 yyxx130   C  71 Female     0     1         3        0          1 #> 611  yyxx131   C  73   Male     0     1         2        1          0 #> 3491 yyxx132   C  51 Female     1     1         2        0          1 #> 1701 yyxx133   C  53 Female     0     0         3        0          1 #> 3641 yyxx134   C  46 Female     0     1         4        0          1 #> 4710 yyxx135   C 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1 #> 4111 yyxx148   C  65   Male     0     0         4        1          0 #> 1231 yyxx149   C  55   Male     0     1         3        1          0 #> 2531  yyxx15   C  53 Female     1     0         4        0          1 #> 2761 yyxx150   C  52 Female     1     0         1        0          1 #> 3551 yyxx151   C  65   Male     0     1         2        1          0 #> 1841 yyxx152   C  64   Male     0     1         3        1          0 #> 621  yyxx153   C  60 Female     1     1         3        0          1 #> 1801 yyxx154   C  58 Female     0     0         3        0          1 #> 4051 yyxx155   C  49   Male     0     0         4        1          0 #> 3081 yyxx156   C  72   Male     0     0         2        1          0 #> 2191 yyxx157   C  71 Female     0     0         3        0          1 #> 4141 yyxx158   C  71 Female     0     0         2        0          1 #> 1991 yyxx159   C  62 Female     1     0         1        0          1 #> 911   yyxx16   C  51   Male     1     0        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Male     1     0         3        1          0 #> 1071 yyxx173   C  72 Female     1     0         2        0          1 #> 3331 yyxx174   C  45 Female     0     0         1        0          1 #> 2121 yyxx175   C  52   Male     1     0         2        1          0 #> 2361 yyxx176   C  62 Female     0     1         1        0          1 #> 4511 yyxx177   C  55 Female     0     0         1        0          1 #> 2371 yyxx178   C  70 Female     1     0         3        0          1 #> 2381 yyxx179   C  60 Female     1     0         2        0          1 #> 4591  yyxx18   C  59   Male     1     0         4        1          0 #> 3791 yyxx180   C  61   Male     0     1         1        1          0 #> 2851 yyxx181   C  56 Female     1     1         4        0          1 #> 4941 yyxx182   C  71 Female     0     1         2        0          1 #> 4071 yyxx183   C  67 Female     0     1         4        0          1 #> 3291 yyxx184   C  48 Female     0     0         1        0          1 #> 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  1         3        1          0 #> 1391 yyxx494   C  67 Female     0     0         2        0          1 #> 1321 yyxx495   C  51   Male     0     1         3        1          0 #> 3151 yyxx496   C  74   Male     0     0         2        1          0 #> 4310 yyxx497   C  73 Female     1     1         2        0          1 #> 3011 yyxx498   C  53   Male     0     1         2        1          0 #> 1401 yyxx499   C  48 Female     0     0         3        0          1 #> 4181   yyxx5   C  53 Female     0     0         2        0          1 #> 2210  yyxx50   C  64   Male     0     1         3        1          0 #> 1311 yyxx500   C  56 Female     0     1         1        0          1 #> 1591  yyxx51   C  75 Female     0     1         2        0          1 #> 2061  yyxx52   C  67   Male     0     0         1        1          0 #> 4421  yyxx53   C  57 Female     0     1         4        0          1 #> 2991  yyxx54   C  50 Female     0     0         2        0          1 #> 4801  yyxx55   C  46 Female     0     0         3        0          1 #> 1461  yyxx56   C  70 Female     1     1         4        0          1 #> 3141  yyxx57   C  75 Female     0     0         1        0          1 #> 3031  yyxx58   C  58   Male     0     0         4        1          0 #> 2051  yyxx59   C  58 Female     1     0         2        0          1 #> 641    yyxx6   C  69 Female     1     1         2        0          1 #> 1791  yyxx60   C  70   Male     0     0         3        1          0 #> 4531  yyxx61   C  75   Male     1     0         4        1          0 #> 1610  yyxx62   C  66   Male     0     0         2        1          0 #> 4091  yyxx63   C  68 Female     1     1         2        0          1 #> 1491  yyxx64   C  54 Female     0     0         4        0          1 #> 1951  yyxx65   C  52 Female     0     1         3        0          1 #> 1100  yyxx66   C  45   Male     0     0         4        1          0 #> 3611  yyxx67   C  57   Male     0     1         4        1         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  2        0          1 #> 1511  yyxx80   C  49 Female     1     1         3        0          1 #> 3110  yyxx81   C  49   Male     0     1         4        1          0 #> 2521  yyxx82   C  46   Male     0     0         1        1          0 #> 4161  yyxx83   C  54 Female     0     1         3        0          1 #> 3510  yyxx84   C  59 Female     1     1         2        0          1 #> 3241  yyxx85   C  56 Female     0     0         4        0          1 #> 3941  yyxx86   C  73 Female     0     0         4        0          1 #> 4210  yyxx87   C  47 Female     1     1         2        0          1 #> 1751  yyxx88   C  45 Female     1     1         3        0          1 #> 4411  yyxx89   C  64 Female     0     0         3        0          1 #> 2131   yyxx9   C  57 Female     0     1         4        0          1 #> 2351  yyxx90   C  53 Female     0     0         1        0          1 #> 4601  yyxx91   C  46   Male     1     0         4        1          0 #> 3671  yyxx92   C  55   Male     0     0         1        1          0 #> 2981  yyxx93   C  63   Male     0     0         4        1          0 #> 1191  yyxx94   C  71   Male     0     0         1        1          0 #> 2151  yyxx95   C  49 Female     0     0         4        0          1 #> 3071  yyxx96   C  61 Female     0     0         3        0          1 #> 3591  yyxx97   C  53   Male     1     1         2        1          0 #> 2391  yyxx98   C  72   Male     0     0         1        1          0 #> 1381  yyxx99   C  65   Male     0     0         1        1          0"},{"path":"https://hta-pharma.github.io/maicplus/reference/ess_footnote_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Note on Expected Sample Size Reduction — ess_footnote_text","title":"Note on Expected Sample Size Reduction — ess_footnote_text","text":"Note Expected Sample Size Reduction","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ess_footnote_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Note on Expected Sample Size Reduction — ess_footnote_text","text":"","code":"ess_footnote_text(width = 0.9 * getOption(\"width\"))"},{"path":"https://hta-pharma.github.io/maicplus/reference/ess_footnote_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Note on Expected Sample Size Reduction — ess_footnote_text","text":"width Number characters break string new lines (\\n).","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ess_footnote_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Note on Expected Sample Size Reduction — ess_footnote_text","text":"character string","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/estimate_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive individual weights in the matching step of MAIC — estimate_weights","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"Assuming data properly processed, function takes individual patient data (IPD) centered covariates (effect modifiers /prognostic variables) input, generates weights individual IPD trial match covariates aggregate data. plot function displays individuals weights key summary top right legend includes median weight, effective sample size (ESS), reduction percentage (percent ESS reduced original sample size). two options plotting: base R plot ggplot. default base R plot plot unscaled scaled separately. default ggplot plot unscaled scaled weights plot.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/estimate_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"","code":"estimate_weights(   data,   centered_colnames = NULL,   start_val = 0,   method = \"BFGS\",   n_boot_iteration = NULL,   set_seed_boot = 1234,   ... )  # S3 method for maicplus_estimate_weights plot(   x,   ggplot = FALSE,   bin_col = \"#6ECEB2\",   vline_col = \"#688CE8\",   main_title = NULL,   scaled_weights = TRUE,   bins = 50,   ... )"},{"path":"https://hta-pharma.github.io/maicplus/reference/estimate_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"data numeric matrix, centered covariates IPD, missing value cell allowed centered_colnames character numeric vector (column indicators) centered covariates start_val scalar, starting value coefficients propensity score regression method string, name optimization algorithm (see 'method' argument base::optim()) default \"BFGS\", options \"Nelder-Mead\", \"CG\", \"L-BFGS-B\", \"SANN\", \"Brent\" n_boot_iteration integer, number bootstrap iterations. default NULL means bootstrapping procedure triggered, hence element \"boot\" output list object NULL. set_seed_boot scalar, random seed conducting bootstrapping, relevant n_boot_iteration NULL. default, use seed 1234 ... Additional control parameters passed stats::optim. x object estimate_weights ggplot indicator print base weights plot ggplot weights plot bin_col string, color bins histogram vline_col string, color vertical line histogram main_title title plot. ggplot, name scaled weights plot unscaled weights plot, respectively. scaled_weights (base plot ) indicator using scaled weights instead regular weights bins (ggplot ) number bin parameter use","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/estimate_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"list following 4 elements, data data.frame, includes input data appended column 'weights' 'scaled_weights'. Scaled weights summation number rows data missing value effect modifiers centered_colnames column names centered effect modifiers data nr_missing number rows data least 1 missing value specified centered effect modifiers ess effective sample size, square sum divided sum squares opt R object returned base::optim(), assess convergence details boot n 2 k array NA, n equals number rows data, k equals n_boot_iteration. 2 columns second dimension include column numeric indexes rows data selected bootstrapping iteration column weights. boot NA argument n_boot_iteration set NULL","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/estimate_weights.html","id":"methods-by-generic-","dir":"Reference","previous_headings":"","what":"Methods (by generic)","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"plot(maicplus_estimate_weights): Plot method estimate_weights objects","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/estimate_weights.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Derive individual weights in the matching step of MAIC — estimate_weights","text":"","code":"data(agd) data(adsl_sat) ipd_centered <- center_ipd(ipd = adsl_sat, agd = process_agd(agd)) centered_colnames <- grep(\"_CENTERED\", colnames(ipd_centered), value = TRUE) centered_colnames #> [1] \"AGE_CENTERED\"              \"AGE_MEDIAN_CENTERED\"       #> [3] \"AGE_SQUARED_CENTERED\"      \"SEX_MALE_CENTERED\"         #> [5] \"ECOG0_CENTERED\"            \"SMOKE_CENTERED\"            #> [7] \"N_PR_THER_MEDIAN_CENTERED\" weighted_data <- estimate_weights(data = ipd_centered, centered_colnames = centered_colnames) # \\donttest{ # To later estimate bootstrap confidence intervals, we calculate the weights # for the bootstrap samples: weighted_data_boot <- estimate_weights(   data = ipd_centered, centered_colnames = centered_colnames, n_boot_iteration = 500 ) # } plot(weighted_sat)   if (requireNamespace(\"ggplot2\")) {   plot(weighted_sat, ggplot = TRUE) } #> Loading required namespace: ggplot2"},{"path":"https://hta-pharma.github.io/maicplus/reference/ext_tte_transfer.html","id":null,"dir":"Reference","previous_headings":"","what":"helper function: transform TTE ADaM data to suitable input for survival R package — ext_tte_transfer","title":"helper function: transform TTE ADaM data to suitable input for survival R package — ext_tte_transfer","text":"helper function: transform TTE ADaM data suitable input survival R package","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ext_tte_transfer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"helper function: transform TTE ADaM data to suitable input for survival R package — ext_tte_transfer","text":"","code":"ext_tte_transfer(dd, time_scale = \"months\", trt = NULL)"},{"path":"https://hta-pharma.github.io/maicplus/reference/ext_tte_transfer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"helper function: transform TTE ADaM data to suitable input for survival R package — ext_tte_transfer","text":"dd data frame, ADTTE read via haven::read_sas time_scale character string, 'years', 'months', 'weeks' 'days', time unit median survival time trt values include treatment column","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ext_tte_transfer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"helper function: transform TTE ADaM data to suitable input for survival R package — ext_tte_transfer","text":"data frame can used input survival::Surv","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/find_SE_from_CI.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","title":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","text":"Comparator studies often report confidence interval treatment effects. function calculates standard error treatment effect given reported confidence interval. relative treatment effect (.e. hazard ratio, odds ratio, risk ratio), function log confidence interval. risk difference mean difference, log confidence interval. option log confidence interval controlled 'logged' parameter.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/find_SE_from_CI.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","text":"","code":"find_SE_from_CI(   CI_lower = NULL,   CI_upper = NULL,   CI_perc = 0.95,   logged = TRUE )"},{"path":"https://hta-pharma.github.io/maicplus/reference/find_SE_from_CI.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","text":"CI_lower Reported lower percentile value treatment effect CI_upper Reported upper percentile value treatment effect CI_perc Percentage confidence interval reported logged Whether confidence interval logged. relative treatment effect, log applied estimated log treatment effect approximately normally distributed.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/find_SE_from_CI.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","text":"Standard error log relative treatment effect 'logged' true standard error treatment effect 'logged' false","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/find_SE_from_CI.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate standard error from the reported confidence interval. — find_SE_from_CI","text":"","code":"find_SE_from_CI(CI_lower = 0.55, CI_upper = 0.90, CI_perc = 0.95) #> [1] 0.1256341"},{"path":"https://hta-pharma.github.io/maicplus/reference/generate_survival_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate time-to-event data — generate_survival_data","title":"Generate time-to-event data — generate_survival_data","text":"Function generates time--event data. follow data-generating mechanism Bender et al. Remiro-Azocar et al. 2021 simulate Weibull-distributed survival times proportional hazards parameterization. per Remiro-Azocar et al, set default scale parameter (lambda) 8.5 shape parameter (nu) 1.3 reflect frequently observed mortality trends metastatic cancer patients. Censoring times generated exponential distribution rate parameter 0.96 selected achieve censoring rate 35% active treatment baseline (values covariates set zero).","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/generate_survival_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate time-to-event data — generate_survival_data","text":"","code":"generate_survival_data(   N = 150,   ncov = 4,   lambda = 8.5,   nu = 1.3,   censor_rate = 0.96,   cor = 0.35,   marginal_mean = 0.6,   marginal_sd = 0.2,   beta = rep(-log(0.67), ncov),   gamma = sort(rep(c(0, -log(0.67)), length = ncov)),   d0 = log(0.25),   seed = 1 )"},{"path":"https://hta-pharma.github.io/maicplus/reference/generate_survival_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate time-to-event data — generate_survival_data","text":"N sample size ncov number covariates generate lambda scale parameter baseline hazard function nu shape parameter baseline hazard function censor_rate rate parameter exponential distribution censoring times cor correlation covariates marginal_mean marginal mean covariates marginal_sd marginal standard deviation covariates beta vector coefficients prognostic factors. Defaults -log(0.67) covariates. gamma vector coefficients effect modifiers. Default 0 half covariates -log(0.67) others. d0 coefficient treatment effect patients treat = 0 seed seed used simulation reproducibility","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/generate_survival_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate time-to-event data — generate_survival_data","text":"data.frame survival time, censor (event) status, treatment indicator, covariates","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/generate_survival_data.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Generate time-to-event data — generate_survival_data","text":"Remiro-Azocar et al. 2021","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/get_pseudo_ipd_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","title":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","text":"Create pseudo IPD given aggregated binary data","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/get_pseudo_ipd_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","text":"","code":"get_pseudo_ipd_binary(binary_agd, format = c(\"stacked\", \"unstacked\"))"},{"path":"https://hta-pharma.github.io/maicplus/reference/get_pseudo_ipd_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","text":"binary_agd data.frame take different formats depending format format string, \"stacked\" \"unstacked\"","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/get_pseudo_ipd_binary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","text":"data.frame pseudo binary IPD, columns USUBJID, ARM, RESPONSE","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/get_pseudo_ipd_binary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create pseudo IPD given aggregated binary data — get_pseudo_ipd_binary","text":"","code":"# example of unstacked testdat <- data.frame(Yes = 280, No = 120) rownames(testdat) <- \"B\" get_pseudo_ipd_binary(   binary_agd = testdat,   format = \"unstacked\" ) #>                    USUBJID ARM RESPONSE #> 1     pseudo_binary_subj_1   B     TRUE #> 2     pseudo_binary_subj_2   B     TRUE #> 3     pseudo_binary_subj_3   B     TRUE #> 4     pseudo_binary_subj_4   B     TRUE #> 5     pseudo_binary_subj_5   B     TRUE #> 6     pseudo_binary_subj_6   B     TRUE #> 7     pseudo_binary_subj_7   B     TRUE #> 8     pseudo_binary_subj_8   B     TRUE #> 9     pseudo_binary_subj_9   B     TRUE #> 10   pseudo_binary_subj_10   B     TRUE #> 11   pseudo_binary_subj_11   B     TRUE #> 12   pseudo_binary_subj_12   B     TRUE #> 13   pseudo_binary_subj_13   B     TRUE #> 14   pseudo_binary_subj_14   B     TRUE #> 15   pseudo_binary_subj_15   B     TRUE #> 16   pseudo_binary_subj_16   B     TRUE #> 17   pseudo_binary_subj_17   B     TRUE #> 18   pseudo_binary_subj_18   B     TRUE #> 19   pseudo_binary_subj_19   B     TRUE #> 20   pseudo_binary_subj_20   B     TRUE #> 21   pseudo_binary_subj_21   B     TRUE #> 22   pseudo_binary_subj_22   B     TRUE #> 23   pseudo_binary_subj_23   B     TRUE #> 24   pseudo_binary_subj_24   B     TRUE #> 25   pseudo_binary_subj_25   B     TRUE #> 26   pseudo_binary_subj_26   B     TRUE #> 27   pseudo_binary_subj_27   B     TRUE #> 28   pseudo_binary_subj_28   B     TRUE #> 29   pseudo_binary_subj_29   B     TRUE #> 30   pseudo_binary_subj_30   B     TRUE #> 31   pseudo_binary_subj_31   B     TRUE #> 32   pseudo_binary_subj_32   B     TRUE #> 33   pseudo_binary_subj_33   B     TRUE #> 34   pseudo_binary_subj_34   B     TRUE #> 35   pseudo_binary_subj_35   B     TRUE #> 36   pseudo_binary_subj_36   B     TRUE #> 37   pseudo_binary_subj_37   B     TRUE #> 38   pseudo_binary_subj_38   B     TRUE #> 39   pseudo_binary_subj_39   B     TRUE #> 40   pseudo_binary_subj_40   B     TRUE #> 41   pseudo_binary_subj_41   B     TRUE #> 42   pseudo_binary_subj_42   B     TRUE #> 43   pseudo_binary_subj_43   B     TRUE #> 44   pseudo_binary_subj_44   B     TRUE #> 45   pseudo_binary_subj_45   B     TRUE #> 46   pseudo_binary_subj_46   B     TRUE #> 47   pseudo_binary_subj_47   B     TRUE #> 48   pseudo_binary_subj_48   B     TRUE #> 49   pseudo_binary_subj_49   B     TRUE #> 50   pseudo_binary_subj_50   B     TRUE #> 51   pseudo_binary_subj_51   B     TRUE #> 52   pseudo_binary_subj_52   B     TRUE #> 53   pseudo_binary_subj_53   B     TRUE #> 54   pseudo_binary_subj_54   B     TRUE #> 55   pseudo_binary_subj_55   B     TRUE #> 56   pseudo_binary_subj_56   B     TRUE #> 57   pseudo_binary_subj_57   B     TRUE #> 58   pseudo_binary_subj_58   B     TRUE #> 59   pseudo_binary_subj_59   B     TRUE #> 60   pseudo_binary_subj_60   B     TRUE #> 61   pseudo_binary_subj_61   B     TRUE #> 62   pseudo_binary_subj_62   B     TRUE #> 63   pseudo_binary_subj_63   B     TRUE #> 64   pseudo_binary_subj_64   B     TRUE #> 65   pseudo_binary_subj_65   B     TRUE #> 66   pseudo_binary_subj_66   B     TRUE #> 67   pseudo_binary_subj_67   B     TRUE #> 68   pseudo_binary_subj_68   B     TRUE #> 69   pseudo_binary_subj_69   B     TRUE #> 70   pseudo_binary_subj_70   B     TRUE #> 71   pseudo_binary_subj_71   B     TRUE #> 72   pseudo_binary_subj_72   B     TRUE #> 73   pseudo_binary_subj_73   B     TRUE #> 74   pseudo_binary_subj_74   B     TRUE #> 75   pseudo_binary_subj_75   B     TRUE #> 76   pseudo_binary_subj_76   B     TRUE #> 77   pseudo_binary_subj_77   B     TRUE #> 78   pseudo_binary_subj_78   B     TRUE #> 79   pseudo_binary_subj_79   B     TRUE #> 80   pseudo_binary_subj_80   B     TRUE #> 81   pseudo_binary_subj_81   B     TRUE #> 82   pseudo_binary_subj_82   B     TRUE #> 83   pseudo_binary_subj_83   B     TRUE #> 84   pseudo_binary_subj_84   B     TRUE #> 85   pseudo_binary_subj_85   B     TRUE #> 86   pseudo_binary_subj_86   B     TRUE #> 87   pseudo_binary_subj_87   B     TRUE #> 88   pseudo_binary_subj_88   B     TRUE #> 89   pseudo_binary_subj_89   B     TRUE #> 90   pseudo_binary_subj_90   B     TRUE #> 91   pseudo_binary_subj_91   B     TRUE #> 92   pseudo_binary_subj_92   B     TRUE #> 93   pseudo_binary_subj_93   B     TRUE #> 94   pseudo_binary_subj_94   B     TRUE #> 95   pseudo_binary_subj_95   B     TRUE #> 96   pseudo_binary_subj_96   B     TRUE #> 97   pseudo_binary_subj_97   B     TRUE #> 98   pseudo_binary_subj_98   B     TRUE #> 99   pseudo_binary_subj_99   B     TRUE #> 100 pseudo_binary_subj_100   B     TRUE #> 101 pseudo_binary_subj_101   B     TRUE #> 102 pseudo_binary_subj_102   B     TRUE #> 103 pseudo_binary_subj_103   B     TRUE #> 104 pseudo_binary_subj_104   B     TRUE #> 105 pseudo_binary_subj_105   B     TRUE #> 106 pseudo_binary_subj_106   B     TRUE #> 107 pseudo_binary_subj_107   B     TRUE #> 108 pseudo_binary_subj_108   B     TRUE #> 109 pseudo_binary_subj_109   B     TRUE #> 110 pseudo_binary_subj_110   B     TRUE #> 111 pseudo_binary_subj_111   B     TRUE #> 112 pseudo_binary_subj_112   B     TRUE #> 113 pseudo_binary_subj_113   B     TRUE #> 114 pseudo_binary_subj_114   B     TRUE #> 115 pseudo_binary_subj_115   B     TRUE #> 116 pseudo_binary_subj_116   B     TRUE #> 117 pseudo_binary_subj_117   B     TRUE #> 118 pseudo_binary_subj_118   B     TRUE #> 119 pseudo_binary_subj_119   B     TRUE #> 120 pseudo_binary_subj_120   B     TRUE #> 121 pseudo_binary_subj_121   B     TRUE #> 122 pseudo_binary_subj_122   B     TRUE #> 123 pseudo_binary_subj_123   B     TRUE #> 124 pseudo_binary_subj_124   B     TRUE #> 125 pseudo_binary_subj_125   B     TRUE #> 126 pseudo_binary_subj_126   B     TRUE #> 127 pseudo_binary_subj_127   B     TRUE #> 128 pseudo_binary_subj_128   B     TRUE #> 129 pseudo_binary_subj_129   B     TRUE #> 130 pseudo_binary_subj_130   B     TRUE #> 131 pseudo_binary_subj_131   B     TRUE #> 132 pseudo_binary_subj_132   B     TRUE #> 133 pseudo_binary_subj_133   B     TRUE #> 134 pseudo_binary_subj_134   B     TRUE #> 135 pseudo_binary_subj_135   B     TRUE #> 136 pseudo_binary_subj_136   B     TRUE #> 137 pseudo_binary_subj_137   B     TRUE #> 138 pseudo_binary_subj_138   B     TRUE #> 139 pseudo_binary_subj_139   B     TRUE #> 140 pseudo_binary_subj_140   B     TRUE #> 141 pseudo_binary_subj_141   B     TRUE #> 142 pseudo_binary_subj_142   B     TRUE #> 143 pseudo_binary_subj_143   B     TRUE #> 144 pseudo_binary_subj_144   B     TRUE #> 145 pseudo_binary_subj_145   B     TRUE #> 146 pseudo_binary_subj_146   B     TRUE #> 147 pseudo_binary_subj_147   B     TRUE #> 148 pseudo_binary_subj_148   B     TRUE #> 149 pseudo_binary_subj_149   B     TRUE #> 150 pseudo_binary_subj_150   B     TRUE #> 151 pseudo_binary_subj_151   B     TRUE #> 152 pseudo_binary_subj_152   B     TRUE #> 153 pseudo_binary_subj_153   B     TRUE #> 154 pseudo_binary_subj_154   B     TRUE #> 155 pseudo_binary_subj_155   B     TRUE #> 156 pseudo_binary_subj_156   B     TRUE #> 157 pseudo_binary_subj_157   B     TRUE #> 158 pseudo_binary_subj_158   B     TRUE #> 159 pseudo_binary_subj_159   B     TRUE #> 160 pseudo_binary_subj_160   B     TRUE #> 161 pseudo_binary_subj_161   B     TRUE #> 162 pseudo_binary_subj_162   B     TRUE #> 163 pseudo_binary_subj_163   B     TRUE #> 164 pseudo_binary_subj_164   B     TRUE #> 165 pseudo_binary_subj_165   B     TRUE #> 166 pseudo_binary_subj_166   B     TRUE #> 167 pseudo_binary_subj_167   B     TRUE #> 168 pseudo_binary_subj_168   B     TRUE #> 169 pseudo_binary_subj_169   B     TRUE #> 170 pseudo_binary_subj_170   B     TRUE #> 171 pseudo_binary_subj_171   B     TRUE #> 172 pseudo_binary_subj_172   B     TRUE #> 173 pseudo_binary_subj_173   B     TRUE #> 174 pseudo_binary_subj_174   B     TRUE #> 175 pseudo_binary_subj_175   B     TRUE #> 176 pseudo_binary_subj_176   B     TRUE #> 177 pseudo_binary_subj_177   B     TRUE #> 178 pseudo_binary_subj_178   B     TRUE #> 179 pseudo_binary_subj_179   B     TRUE #> 180 pseudo_binary_subj_180   B     TRUE #> 181 pseudo_binary_subj_181   B     TRUE #> 182 pseudo_binary_subj_182   B     TRUE #> 183 pseudo_binary_subj_183   B     TRUE #> 184 pseudo_binary_subj_184   B     TRUE #> 185 pseudo_binary_subj_185   B     TRUE #> 186 pseudo_binary_subj_186   B     TRUE #> 187 pseudo_binary_subj_187   B     TRUE #> 188 pseudo_binary_subj_188   B     TRUE #> 189 pseudo_binary_subj_189   B     TRUE #> 190 pseudo_binary_subj_190   B     TRUE #> 191 pseudo_binary_subj_191   B     TRUE #> 192 pseudo_binary_subj_192   B     TRUE #> 193 pseudo_binary_subj_193   B     TRUE #> 194 pseudo_binary_subj_194   B     TRUE #> 195 pseudo_binary_subj_195   B     TRUE #> 196 pseudo_binary_subj_196   B     TRUE #> 197 pseudo_binary_subj_197   B     TRUE #> 198 pseudo_binary_subj_198   B     TRUE #> 199 pseudo_binary_subj_199   B     TRUE #> 200 pseudo_binary_subj_200   B     TRUE #> 201 pseudo_binary_subj_201   B     TRUE #> 202 pseudo_binary_subj_202   B     TRUE #> 203 pseudo_binary_subj_203   B     TRUE #> 204 pseudo_binary_subj_204   B     TRUE #> 205 pseudo_binary_subj_205   B     TRUE #> 206 pseudo_binary_subj_206   B     TRUE #> 207 pseudo_binary_subj_207   B     TRUE #> 208 pseudo_binary_subj_208   B     TRUE #> 209 pseudo_binary_subj_209   B     TRUE #> 210 pseudo_binary_subj_210   B     TRUE #> 211 pseudo_binary_subj_211   B     TRUE #> 212 pseudo_binary_subj_212   B     TRUE #> 213 pseudo_binary_subj_213   B     TRUE #> 214 pseudo_binary_subj_214   B     TRUE #> 215 pseudo_binary_subj_215   B     TRUE #> 216 pseudo_binary_subj_216   B     TRUE #> 217 pseudo_binary_subj_217   B     TRUE #> 218 pseudo_binary_subj_218   B     TRUE #> 219 pseudo_binary_subj_219   B     TRUE #> 220 pseudo_binary_subj_220   B     TRUE #> 221 pseudo_binary_subj_221   B     TRUE #> 222 pseudo_binary_subj_222   B     TRUE #> 223 pseudo_binary_subj_223   B     TRUE #> 224 pseudo_binary_subj_224   B     TRUE #> 225 pseudo_binary_subj_225   B     TRUE #> 226 pseudo_binary_subj_226   B     TRUE #> 227 pseudo_binary_subj_227   B     TRUE #> 228 pseudo_binary_subj_228   B     TRUE #> 229 pseudo_binary_subj_229   B     TRUE #> 230 pseudo_binary_subj_230   B     TRUE #> 231 pseudo_binary_subj_231   B     TRUE #> 232 pseudo_binary_subj_232   B     TRUE #> 233 pseudo_binary_subj_233   B     TRUE #> 234 pseudo_binary_subj_234   B     TRUE #> 235 pseudo_binary_subj_235   B     TRUE #> 236 pseudo_binary_subj_236   B     TRUE #> 237 pseudo_binary_subj_237   B     TRUE #> 238 pseudo_binary_subj_238   B     TRUE #> 239 pseudo_binary_subj_239   B     TRUE #> 240 pseudo_binary_subj_240   B     TRUE #> 241 pseudo_binary_subj_241   B     TRUE #> 242 pseudo_binary_subj_242   B     TRUE #> 243 pseudo_binary_subj_243   B     TRUE #> 244 pseudo_binary_subj_244   B     TRUE #> 245 pseudo_binary_subj_245   B     TRUE #> 246 pseudo_binary_subj_246   B     TRUE #> 247 pseudo_binary_subj_247   B     TRUE #> 248 pseudo_binary_subj_248   B     TRUE #> 249 pseudo_binary_subj_249   B     TRUE #> 250 pseudo_binary_subj_250   B     TRUE #> 251 pseudo_binary_subj_251   B     TRUE #> 252 pseudo_binary_subj_252   B     TRUE #> 253 pseudo_binary_subj_253   B     TRUE #> 254 pseudo_binary_subj_254   B     TRUE #> 255 pseudo_binary_subj_255   B     TRUE #> 256 pseudo_binary_subj_256   B     TRUE #> 257 pseudo_binary_subj_257   B     TRUE #> 258 pseudo_binary_subj_258   B     TRUE #> 259 pseudo_binary_subj_259   B     TRUE #> 260 pseudo_binary_subj_260   B     TRUE #> 261 pseudo_binary_subj_261   B     TRUE #> 262 pseudo_binary_subj_262   B     TRUE #> 263 pseudo_binary_subj_263   B     TRUE #> 264 pseudo_binary_subj_264   B     TRUE #> 265 pseudo_binary_subj_265   B     TRUE #> 266 pseudo_binary_subj_266   B     TRUE #> 267 pseudo_binary_subj_267   B     TRUE #> 268 pseudo_binary_subj_268   B     TRUE #> 269 pseudo_binary_subj_269   B     TRUE #> 270 pseudo_binary_subj_270   B     TRUE #> 271 pseudo_binary_subj_271   B     TRUE #> 272 pseudo_binary_subj_272   B     TRUE #> 273 pseudo_binary_subj_273   B     TRUE #> 274 pseudo_binary_subj_274   B     TRUE #> 275 pseudo_binary_subj_275   B     TRUE #> 276 pseudo_binary_subj_276   B     TRUE #> 277 pseudo_binary_subj_277   B     TRUE #> 278 pseudo_binary_subj_278   B     TRUE #> 279 pseudo_binary_subj_279   B     TRUE #> 280 pseudo_binary_subj_280   B     TRUE #> 281 pseudo_binary_subj_281   B    FALSE #> 282 pseudo_binary_subj_282   B    FALSE #> 283 pseudo_binary_subj_283   B    FALSE #> 284 pseudo_binary_subj_284   B    FALSE #> 285 pseudo_binary_subj_285   B    FALSE #> 286 pseudo_binary_subj_286   B    FALSE #> 287 pseudo_binary_subj_287   B    FALSE #> 288 pseudo_binary_subj_288   B    FALSE #> 289 pseudo_binary_subj_289   B    FALSE #> 290 pseudo_binary_subj_290   B    FALSE #> 291 pseudo_binary_subj_291   B    FALSE #> 292 pseudo_binary_subj_292   B    FALSE #> 293 pseudo_binary_subj_293   B    FALSE #> 294 pseudo_binary_subj_294   B    FALSE #> 295 pseudo_binary_subj_295   B    FALSE #> 296 pseudo_binary_subj_296   B    FALSE #> 297 pseudo_binary_subj_297   B    FALSE #> 298 pseudo_binary_subj_298   B    FALSE #> 299 pseudo_binary_subj_299   B    FALSE #> 300 pseudo_binary_subj_300   B    FALSE #> 301 pseudo_binary_subj_301   B    FALSE #> 302 pseudo_binary_subj_302   B    FALSE #> 303 pseudo_binary_subj_303   B    FALSE #> 304 pseudo_binary_subj_304   B    FALSE #> 305 pseudo_binary_subj_305   B    FALSE #> 306 pseudo_binary_subj_306   B    FALSE #> 307 pseudo_binary_subj_307   B    FALSE #> 308 pseudo_binary_subj_308   B    FALSE #> 309 pseudo_binary_subj_309   B    FALSE #> 310 pseudo_binary_subj_310   B    FALSE #> 311 pseudo_binary_subj_311   B    FALSE #> 312 pseudo_binary_subj_312   B    FALSE #> 313 pseudo_binary_subj_313   B    FALSE #> 314 pseudo_binary_subj_314   B    FALSE #> 315 pseudo_binary_subj_315   B    FALSE #> 316 pseudo_binary_subj_316   B    FALSE #> 317 pseudo_binary_subj_317   B    FALSE #> 318 pseudo_binary_subj_318   B    FALSE #> 319 pseudo_binary_subj_319   B    FALSE #> 320 pseudo_binary_subj_320   B    FALSE #> 321 pseudo_binary_subj_321   B    FALSE #> 322 pseudo_binary_subj_322   B    FALSE #> 323 pseudo_binary_subj_323   B    FALSE #> 324 pseudo_binary_subj_324   B    FALSE #> 325 pseudo_binary_subj_325   B    FALSE #> 326 pseudo_binary_subj_326   B    FALSE #> 327 pseudo_binary_subj_327   B    FALSE #> 328 pseudo_binary_subj_328   B    FALSE #> 329 pseudo_binary_subj_329   B    FALSE #> 330 pseudo_binary_subj_330   B    FALSE #> 331 pseudo_binary_subj_331   B    FALSE #> 332 pseudo_binary_subj_332   B    FALSE #> 333 pseudo_binary_subj_333   B    FALSE #> 334 pseudo_binary_subj_334   B    FALSE #> 335 pseudo_binary_subj_335   B    FALSE #> 336 pseudo_binary_subj_336   B    FALSE #> 337 pseudo_binary_subj_337   B    FALSE #> 338 pseudo_binary_subj_338   B    FALSE #> 339 pseudo_binary_subj_339   B    FALSE #> 340 pseudo_binary_subj_340   B    FALSE #> 341 pseudo_binary_subj_341   B    FALSE #> 342 pseudo_binary_subj_342   B    FALSE #> 343 pseudo_binary_subj_343   B    FALSE #> 344 pseudo_binary_subj_344   B    FALSE #> 345 pseudo_binary_subj_345   B    FALSE #> 346 pseudo_binary_subj_346   B    FALSE #> 347 pseudo_binary_subj_347   B    FALSE #> 348 pseudo_binary_subj_348   B    FALSE #> 349 pseudo_binary_subj_349   B    FALSE #> 350 pseudo_binary_subj_350   B    FALSE #> 351 pseudo_binary_subj_351   B    FALSE #> 352 pseudo_binary_subj_352   B    FALSE #> 353 pseudo_binary_subj_353   B    FALSE #> 354 pseudo_binary_subj_354   B    FALSE #> 355 pseudo_binary_subj_355   B    FALSE #> 356 pseudo_binary_subj_356   B    FALSE #> 357 pseudo_binary_subj_357   B    FALSE #> 358 pseudo_binary_subj_358   B    FALSE #> 359 pseudo_binary_subj_359   B    FALSE #> 360 pseudo_binary_subj_360   B    FALSE #> 361 pseudo_binary_subj_361   B    FALSE #> 362 pseudo_binary_subj_362   B    FALSE #> 363 pseudo_binary_subj_363   B    FALSE #> 364 pseudo_binary_subj_364   B    FALSE #> 365 pseudo_binary_subj_365   B    FALSE #> 366 pseudo_binary_subj_366   B    FALSE #> 367 pseudo_binary_subj_367   B    FALSE #> 368 pseudo_binary_subj_368   B    FALSE #> 369 pseudo_binary_subj_369   B    FALSE #> 370 pseudo_binary_subj_370   B    FALSE #> 371 pseudo_binary_subj_371   B    FALSE #> 372 pseudo_binary_subj_372   B    FALSE #> 373 pseudo_binary_subj_373   B    FALSE #> 374 pseudo_binary_subj_374   B    FALSE #> 375 pseudo_binary_subj_375   B    FALSE #> 376 pseudo_binary_subj_376   B    FALSE #> 377 pseudo_binary_subj_377   B    FALSE #> 378 pseudo_binary_subj_378   B    FALSE #> 379 pseudo_binary_subj_379   B    FALSE #> 380 pseudo_binary_subj_380   B    FALSE #> 381 pseudo_binary_subj_381   B    FALSE #> 382 pseudo_binary_subj_382   B    FALSE #> 383 pseudo_binary_subj_383   B    FALSE #> 384 pseudo_binary_subj_384   B    FALSE #> 385 pseudo_binary_subj_385   B    FALSE #> 386 pseudo_binary_subj_386   B    FALSE #> 387 pseudo_binary_subj_387   B    FALSE #> 388 pseudo_binary_subj_388   B    FALSE #> 389 pseudo_binary_subj_389   B    FALSE #> 390 pseudo_binary_subj_390   B    FALSE #> 391 pseudo_binary_subj_391   B    FALSE #> 392 pseudo_binary_subj_392   B    FALSE #> 393 pseudo_binary_subj_393   B    FALSE #> 394 pseudo_binary_subj_394   B    FALSE #> 395 pseudo_binary_subj_395   B    FALSE #> 396 pseudo_binary_subj_396   B    FALSE #> 397 pseudo_binary_subj_397   B    FALSE #> 398 pseudo_binary_subj_398   B    FALSE #> 399 pseudo_binary_subj_399   B    FALSE #> 400 pseudo_binary_subj_400   B    FALSE  # example of stacked get_pseudo_ipd_binary(   binary_agd = data.frame(     ARM = rep(\"B\", 2),     RESPONSE = c(\"YES\", \"NO\"),     COUNT = c(280, 120)   ),   format = \"stacked\" ) #>                    USUBJID ARM RESPONSE #> 1     pseudo_binary_subj_1   B     TRUE #> 2     pseudo_binary_subj_2   B     TRUE #> 3     pseudo_binary_subj_3   B     TRUE #> 4     pseudo_binary_subj_4   B     TRUE #> 5     pseudo_binary_subj_5   B     TRUE #> 6     pseudo_binary_subj_6   B     TRUE #> 7     pseudo_binary_subj_7   B     TRUE #> 8     pseudo_binary_subj_8   B     TRUE #> 9     pseudo_binary_subj_9   B     TRUE #> 10   pseudo_binary_subj_10   B     TRUE #> 11   pseudo_binary_subj_11   B     TRUE #> 12   pseudo_binary_subj_12   B     TRUE #> 13   pseudo_binary_subj_13   B     TRUE #> 14   pseudo_binary_subj_14   B     TRUE #> 15   pseudo_binary_subj_15   B     TRUE #> 16   pseudo_binary_subj_16   B     TRUE #> 17   pseudo_binary_subj_17   B     TRUE #> 18   pseudo_binary_subj_18   B     TRUE #> 19   pseudo_binary_subj_19   B     TRUE #> 20   pseudo_binary_subj_20   B     TRUE #> 21   pseudo_binary_subj_21   B     TRUE #> 22   pseudo_binary_subj_22   B     TRUE #> 23   pseudo_binary_subj_23   B     TRUE #> 24   pseudo_binary_subj_24   B     TRUE #> 25   pseudo_binary_subj_25   B     TRUE #> 26   pseudo_binary_subj_26   B     TRUE #> 27   pseudo_binary_subj_27   B     TRUE #> 28   pseudo_binary_subj_28   B     TRUE #> 29   pseudo_binary_subj_29   B     TRUE #> 30   pseudo_binary_subj_30   B     TRUE #> 31   pseudo_binary_subj_31   B     TRUE #> 32   pseudo_binary_subj_32   B     TRUE #> 33   pseudo_binary_subj_33   B     TRUE #> 34   pseudo_binary_subj_34   B     TRUE #> 35   pseudo_binary_subj_35   B     TRUE #> 36   pseudo_binary_subj_36   B     TRUE #> 37   pseudo_binary_subj_37   B     TRUE #> 38   pseudo_binary_subj_38   B     TRUE #> 39   pseudo_binary_subj_39   B     TRUE #> 40   pseudo_binary_subj_40   B     TRUE #> 41   pseudo_binary_subj_41   B     TRUE #> 42   pseudo_binary_subj_42   B     TRUE #> 43   pseudo_binary_subj_43   B     TRUE #> 44   pseudo_binary_subj_44   B     TRUE #> 45   pseudo_binary_subj_45   B     TRUE #> 46   pseudo_binary_subj_46   B     TRUE #> 47   pseudo_binary_subj_47   B     TRUE #> 48   pseudo_binary_subj_48   B     TRUE #> 49   pseudo_binary_subj_49   B     TRUE #> 50   pseudo_binary_subj_50   B     TRUE #> 51   pseudo_binary_subj_51   B     TRUE #> 52   pseudo_binary_subj_52   B     TRUE #> 53   pseudo_binary_subj_53   B     TRUE #> 54   pseudo_binary_subj_54   B     TRUE #> 55   pseudo_binary_subj_55   B     TRUE #> 56   pseudo_binary_subj_56   B     TRUE #> 57   pseudo_binary_subj_57   B     TRUE #> 58   pseudo_binary_subj_58   B     TRUE #> 59   pseudo_binary_subj_59   B     TRUE #> 60   pseudo_binary_subj_60   B     TRUE #> 61   pseudo_binary_subj_61   B     TRUE #> 62   pseudo_binary_subj_62   B     TRUE #> 63   pseudo_binary_subj_63   B     TRUE #> 64   pseudo_binary_subj_64   B     TRUE #> 65   pseudo_binary_subj_65   B     TRUE #> 66   pseudo_binary_subj_66   B     TRUE #> 67   pseudo_binary_subj_67   B     TRUE #> 68   pseudo_binary_subj_68   B     TRUE #> 69   pseudo_binary_subj_69   B     TRUE #> 70   pseudo_binary_subj_70   B     TRUE #> 71   pseudo_binary_subj_71   B     TRUE #> 72   pseudo_binary_subj_72   B     TRUE #> 73   pseudo_binary_subj_73   B     TRUE #> 74   pseudo_binary_subj_74   B     TRUE #> 75   pseudo_binary_subj_75   B     TRUE #> 76   pseudo_binary_subj_76   B     TRUE #> 77   pseudo_binary_subj_77   B     TRUE #> 78   pseudo_binary_subj_78   B     TRUE #> 79   pseudo_binary_subj_79   B     TRUE #> 80   pseudo_binary_subj_80   B     TRUE #> 81   pseudo_binary_subj_81   B     TRUE #> 82   pseudo_binary_subj_82   B     TRUE #> 83   pseudo_binary_subj_83   B     TRUE #> 84   pseudo_binary_subj_84   B     TRUE #> 85   pseudo_binary_subj_85   B     TRUE #> 86   pseudo_binary_subj_86   B     TRUE #> 87   pseudo_binary_subj_87   B     TRUE #> 88   pseudo_binary_subj_88   B     TRUE #> 89   pseudo_binary_subj_89   B     TRUE #> 90   pseudo_binary_subj_90   B     TRUE #> 91   pseudo_binary_subj_91   B     TRUE #> 92   pseudo_binary_subj_92   B     TRUE #> 93   pseudo_binary_subj_93   B     TRUE #> 94   pseudo_binary_subj_94   B     TRUE #> 95   pseudo_binary_subj_95   B     TRUE #> 96   pseudo_binary_subj_96   B     TRUE #> 97   pseudo_binary_subj_97   B     TRUE #> 98   pseudo_binary_subj_98   B     TRUE #> 99   pseudo_binary_subj_99   B     TRUE #> 100 pseudo_binary_subj_100   B     TRUE #> 101 pseudo_binary_subj_101   B     TRUE #> 102 pseudo_binary_subj_102   B     TRUE #> 103 pseudo_binary_subj_103   B     TRUE #> 104 pseudo_binary_subj_104   B     TRUE #> 105 pseudo_binary_subj_105   B     TRUE #> 106 pseudo_binary_subj_106   B     TRUE #> 107 pseudo_binary_subj_107   B     TRUE #> 108 pseudo_binary_subj_108   B     TRUE #> 109 pseudo_binary_subj_109   B     TRUE #> 110 pseudo_binary_subj_110   B     TRUE #> 111 pseudo_binary_subj_111   B     TRUE #> 112 pseudo_binary_subj_112   B     TRUE #> 113 pseudo_binary_subj_113   B     TRUE #> 114 pseudo_binary_subj_114   B     TRUE #> 115 pseudo_binary_subj_115   B     TRUE #> 116 pseudo_binary_subj_116   B     TRUE #> 117 pseudo_binary_subj_117   B     TRUE #> 118 pseudo_binary_subj_118   B     TRUE #> 119 pseudo_binary_subj_119   B     TRUE #> 120 pseudo_binary_subj_120   B     TRUE #> 121 pseudo_binary_subj_121   B     TRUE #> 122 pseudo_binary_subj_122   B     TRUE #> 123 pseudo_binary_subj_123   B     TRUE #> 124 pseudo_binary_subj_124   B     TRUE #> 125 pseudo_binary_subj_125   B     TRUE #> 126 pseudo_binary_subj_126   B     TRUE #> 127 pseudo_binary_subj_127   B     TRUE #> 128 pseudo_binary_subj_128   B     TRUE #> 129 pseudo_binary_subj_129   B     TRUE #> 130 pseudo_binary_subj_130   B     TRUE #> 131 pseudo_binary_subj_131   B     TRUE #> 132 pseudo_binary_subj_132   B     TRUE #> 133 pseudo_binary_subj_133   B     TRUE #> 134 pseudo_binary_subj_134   B     TRUE #> 135 pseudo_binary_subj_135   B     TRUE #> 136 pseudo_binary_subj_136   B     TRUE #> 137 pseudo_binary_subj_137   B     TRUE #> 138 pseudo_binary_subj_138   B     TRUE #> 139 pseudo_binary_subj_139   B     TRUE #> 140 pseudo_binary_subj_140   B     TRUE #> 141 pseudo_binary_subj_141   B     TRUE #> 142 pseudo_binary_subj_142   B     TRUE #> 143 pseudo_binary_subj_143   B     TRUE #> 144 pseudo_binary_subj_144   B     TRUE #> 145 pseudo_binary_subj_145   B     TRUE #> 146 pseudo_binary_subj_146   B     TRUE #> 147 pseudo_binary_subj_147   B     TRUE #> 148 pseudo_binary_subj_148   B     TRUE #> 149 pseudo_binary_subj_149   B     TRUE #> 150 pseudo_binary_subj_150   B     TRUE #> 151 pseudo_binary_subj_151   B     TRUE #> 152 pseudo_binary_subj_152   B     TRUE #> 153 pseudo_binary_subj_153   B     TRUE #> 154 pseudo_binary_subj_154   B     TRUE #> 155 pseudo_binary_subj_155   B     TRUE #> 156 pseudo_binary_subj_156   B     TRUE #> 157 pseudo_binary_subj_157   B     TRUE #> 158 pseudo_binary_subj_158   B     TRUE #> 159 pseudo_binary_subj_159   B     TRUE #> 160 pseudo_binary_subj_160   B     TRUE #> 161 pseudo_binary_subj_161   B     TRUE #> 162 pseudo_binary_subj_162   B     TRUE #> 163 pseudo_binary_subj_163   B     TRUE #> 164 pseudo_binary_subj_164   B     TRUE #> 165 pseudo_binary_subj_165   B     TRUE #> 166 pseudo_binary_subj_166   B     TRUE #> 167 pseudo_binary_subj_167   B     TRUE #> 168 pseudo_binary_subj_168   B     TRUE #> 169 pseudo_binary_subj_169   B     TRUE #> 170 pseudo_binary_subj_170   B     TRUE #> 171 pseudo_binary_subj_171   B     TRUE #> 172 pseudo_binary_subj_172   B     TRUE #> 173 pseudo_binary_subj_173   B     TRUE #> 174 pseudo_binary_subj_174   B     TRUE #> 175 pseudo_binary_subj_175   B     TRUE #> 176 pseudo_binary_subj_176   B     TRUE #> 177 pseudo_binary_subj_177   B     TRUE #> 178 pseudo_binary_subj_178   B     TRUE #> 179 pseudo_binary_subj_179   B     TRUE #> 180 pseudo_binary_subj_180   B     TRUE #> 181 pseudo_binary_subj_181   B     TRUE #> 182 pseudo_binary_subj_182   B     TRUE #> 183 pseudo_binary_subj_183   B     TRUE #> 184 pseudo_binary_subj_184   B     TRUE #> 185 pseudo_binary_subj_185   B     TRUE #> 186 pseudo_binary_subj_186   B     TRUE #> 187 pseudo_binary_subj_187   B     TRUE #> 188 pseudo_binary_subj_188   B     TRUE #> 189 pseudo_binary_subj_189   B     TRUE #> 190 pseudo_binary_subj_190   B     TRUE #> 191 pseudo_binary_subj_191   B     TRUE #> 192 pseudo_binary_subj_192   B     TRUE #> 193 pseudo_binary_subj_193   B     TRUE #> 194 pseudo_binary_subj_194   B     TRUE #> 195 pseudo_binary_subj_195   B     TRUE #> 196 pseudo_binary_subj_196   B     TRUE #> 197 pseudo_binary_subj_197   B     TRUE #> 198 pseudo_binary_subj_198   B     TRUE #> 199 pseudo_binary_subj_199   B     TRUE #> 200 pseudo_binary_subj_200   B     TRUE #> 201 pseudo_binary_subj_201   B     TRUE #> 202 pseudo_binary_subj_202   B     TRUE #> 203 pseudo_binary_subj_203   B     TRUE #> 204 pseudo_binary_subj_204   B     TRUE #> 205 pseudo_binary_subj_205   B     TRUE #> 206 pseudo_binary_subj_206   B     TRUE #> 207 pseudo_binary_subj_207   B     TRUE #> 208 pseudo_binary_subj_208   B     TRUE #> 209 pseudo_binary_subj_209   B     TRUE #> 210 pseudo_binary_subj_210   B     TRUE #> 211 pseudo_binary_subj_211   B     TRUE #> 212 pseudo_binary_subj_212   B     TRUE #> 213 pseudo_binary_subj_213   B     TRUE #> 214 pseudo_binary_subj_214   B     TRUE #> 215 pseudo_binary_subj_215   B     TRUE #> 216 pseudo_binary_subj_216   B     TRUE #> 217 pseudo_binary_subj_217   B     TRUE #> 218 pseudo_binary_subj_218   B     TRUE #> 219 pseudo_binary_subj_219   B     TRUE #> 220 pseudo_binary_subj_220   B     TRUE #> 221 pseudo_binary_subj_221   B     TRUE #> 222 pseudo_binary_subj_222   B     TRUE #> 223 pseudo_binary_subj_223   B     TRUE #> 224 pseudo_binary_subj_224   B     TRUE #> 225 pseudo_binary_subj_225   B     TRUE #> 226 pseudo_binary_subj_226   B     TRUE #> 227 pseudo_binary_subj_227   B     TRUE #> 228 pseudo_binary_subj_228   B     TRUE #> 229 pseudo_binary_subj_229   B     TRUE #> 230 pseudo_binary_subj_230   B     TRUE #> 231 pseudo_binary_subj_231   B     TRUE #> 232 pseudo_binary_subj_232   B     TRUE #> 233 pseudo_binary_subj_233   B     TRUE #> 234 pseudo_binary_subj_234   B     TRUE #> 235 pseudo_binary_subj_235   B     TRUE #> 236 pseudo_binary_subj_236   B     TRUE #> 237 pseudo_binary_subj_237   B     TRUE #> 238 pseudo_binary_subj_238   B     TRUE #> 239 pseudo_binary_subj_239   B     TRUE #> 240 pseudo_binary_subj_240   B     TRUE #> 241 pseudo_binary_subj_241   B     TRUE #> 242 pseudo_binary_subj_242   B     TRUE #> 243 pseudo_binary_subj_243   B     TRUE #> 244 pseudo_binary_subj_244   B     TRUE #> 245 pseudo_binary_subj_245   B     TRUE #> 246 pseudo_binary_subj_246   B     TRUE #> 247 pseudo_binary_subj_247   B     TRUE #> 248 pseudo_binary_subj_248   B     TRUE #> 249 pseudo_binary_subj_249   B     TRUE #> 250 pseudo_binary_subj_250   B     TRUE #> 251 pseudo_binary_subj_251   B     TRUE #> 252 pseudo_binary_subj_252   B     TRUE #> 253 pseudo_binary_subj_253   B     TRUE #> 254 pseudo_binary_subj_254   B     TRUE #> 255 pseudo_binary_subj_255   B     TRUE #> 256 pseudo_binary_subj_256   B     TRUE #> 257 pseudo_binary_subj_257   B     TRUE #> 258 pseudo_binary_subj_258   B     TRUE #> 259 pseudo_binary_subj_259   B     TRUE #> 260 pseudo_binary_subj_260   B     TRUE #> 261 pseudo_binary_subj_261   B     TRUE #> 262 pseudo_binary_subj_262   B     TRUE #> 263 pseudo_binary_subj_263   B     TRUE #> 264 pseudo_binary_subj_264   B     TRUE #> 265 pseudo_binary_subj_265   B     TRUE #> 266 pseudo_binary_subj_266   B     TRUE #> 267 pseudo_binary_subj_267   B     TRUE #> 268 pseudo_binary_subj_268   B     TRUE #> 269 pseudo_binary_subj_269   B     TRUE #> 270 pseudo_binary_subj_270   B     TRUE #> 271 pseudo_binary_subj_271   B     TRUE #> 272 pseudo_binary_subj_272   B     TRUE #> 273 pseudo_binary_subj_273   B     TRUE #> 274 pseudo_binary_subj_274   B     TRUE #> 275 pseudo_binary_subj_275   B     TRUE #> 276 pseudo_binary_subj_276   B     TRUE #> 277 pseudo_binary_subj_277   B     TRUE #> 278 pseudo_binary_subj_278   B     TRUE #> 279 pseudo_binary_subj_279   B     TRUE #> 280 pseudo_binary_subj_280   B     TRUE #> 281 pseudo_binary_subj_281   B    FALSE #> 282 pseudo_binary_subj_282   B    FALSE #> 283 pseudo_binary_subj_283   B    FALSE #> 284 pseudo_binary_subj_284   B    FALSE #> 285 pseudo_binary_subj_285   B    FALSE #> 286 pseudo_binary_subj_286   B    FALSE #> 287 pseudo_binary_subj_287   B    FALSE #> 288 pseudo_binary_subj_288   B    FALSE #> 289 pseudo_binary_subj_289   B    FALSE #> 290 pseudo_binary_subj_290   B    FALSE #> 291 pseudo_binary_subj_291   B    FALSE #> 292 pseudo_binary_subj_292   B    FALSE #> 293 pseudo_binary_subj_293   B    FALSE #> 294 pseudo_binary_subj_294   B    FALSE #> 295 pseudo_binary_subj_295   B    FALSE #> 296 pseudo_binary_subj_296   B    FALSE #> 297 pseudo_binary_subj_297   B    FALSE #> 298 pseudo_binary_subj_298   B    FALSE #> 299 pseudo_binary_subj_299   B    FALSE #> 300 pseudo_binary_subj_300   B    FALSE #> 301 pseudo_binary_subj_301   B    FALSE #> 302 pseudo_binary_subj_302   B    FALSE #> 303 pseudo_binary_subj_303   B    FALSE #> 304 pseudo_binary_subj_304   B    FALSE #> 305 pseudo_binary_subj_305   B    FALSE #> 306 pseudo_binary_subj_306   B    FALSE #> 307 pseudo_binary_subj_307   B    FALSE #> 308 pseudo_binary_subj_308   B    FALSE #> 309 pseudo_binary_subj_309   B    FALSE #> 310 pseudo_binary_subj_310   B    FALSE #> 311 pseudo_binary_subj_311   B    FALSE #> 312 pseudo_binary_subj_312   B    FALSE #> 313 pseudo_binary_subj_313   B    FALSE #> 314 pseudo_binary_subj_314   B    FALSE #> 315 pseudo_binary_subj_315   B    FALSE #> 316 pseudo_binary_subj_316   B    FALSE #> 317 pseudo_binary_subj_317   B    FALSE #> 318 pseudo_binary_subj_318   B    FALSE #> 319 pseudo_binary_subj_319   B    FALSE #> 320 pseudo_binary_subj_320   B    FALSE #> 321 pseudo_binary_subj_321   B    FALSE #> 322 pseudo_binary_subj_322   B    FALSE #> 323 pseudo_binary_subj_323   B    FALSE #> 324 pseudo_binary_subj_324   B    FALSE #> 325 pseudo_binary_subj_325   B    FALSE #> 326 pseudo_binary_subj_326   B    FALSE #> 327 pseudo_binary_subj_327   B    FALSE #> 328 pseudo_binary_subj_328   B    FALSE #> 329 pseudo_binary_subj_329   B    FALSE #> 330 pseudo_binary_subj_330   B    FALSE #> 331 pseudo_binary_subj_331   B    FALSE #> 332 pseudo_binary_subj_332   B    FALSE #> 333 pseudo_binary_subj_333   B    FALSE #> 334 pseudo_binary_subj_334   B    FALSE #> 335 pseudo_binary_subj_335   B    FALSE #> 336 pseudo_binary_subj_336   B    FALSE #> 337 pseudo_binary_subj_337   B    FALSE #> 338 pseudo_binary_subj_338   B    FALSE #> 339 pseudo_binary_subj_339   B    FALSE #> 340 pseudo_binary_subj_340   B    FALSE #> 341 pseudo_binary_subj_341   B    FALSE #> 342 pseudo_binary_subj_342   B    FALSE #> 343 pseudo_binary_subj_343   B    FALSE #> 344 pseudo_binary_subj_344   B    FALSE #> 345 pseudo_binary_subj_345   B    FALSE #> 346 pseudo_binary_subj_346   B    FALSE #> 347 pseudo_binary_subj_347   B    FALSE #> 348 pseudo_binary_subj_348   B    FALSE #> 349 pseudo_binary_subj_349   B    FALSE #> 350 pseudo_binary_subj_350   B    FALSE #> 351 pseudo_binary_subj_351   B    FALSE #> 352 pseudo_binary_subj_352   B    FALSE #> 353 pseudo_binary_subj_353   B    FALSE #> 354 pseudo_binary_subj_354   B    FALSE #> 355 pseudo_binary_subj_355   B    FALSE #> 356 pseudo_binary_subj_356   B    FALSE #> 357 pseudo_binary_subj_357   B    FALSE #> 358 pseudo_binary_subj_358   B    FALSE #> 359 pseudo_binary_subj_359   B    FALSE #> 360 pseudo_binary_subj_360   B    FALSE #> 361 pseudo_binary_subj_361   B    FALSE #> 362 pseudo_binary_subj_362   B    FALSE #> 363 pseudo_binary_subj_363   B    FALSE #> 364 pseudo_binary_subj_364   B    FALSE #> 365 pseudo_binary_subj_365   B    FALSE #> 366 pseudo_binary_subj_366   B    FALSE #> 367 pseudo_binary_subj_367   B    FALSE #> 368 pseudo_binary_subj_368   B    FALSE #> 369 pseudo_binary_subj_369   B    FALSE #> 370 pseudo_binary_subj_370   B    FALSE #> 371 pseudo_binary_subj_371   B    FALSE #> 372 pseudo_binary_subj_372   B    FALSE #> 373 pseudo_binary_subj_373   B    FALSE #> 374 pseudo_binary_subj_374   B    FALSE #> 375 pseudo_binary_subj_375   B    FALSE #> 376 pseudo_binary_subj_376   B    FALSE #> 377 pseudo_binary_subj_377   B    FALSE #> 378 pseudo_binary_subj_378   B    FALSE #> 379 pseudo_binary_subj_379   B    FALSE #> 380 pseudo_binary_subj_380   B    FALSE #> 381 pseudo_binary_subj_381   B    FALSE #> 382 pseudo_binary_subj_382   B    FALSE #> 383 pseudo_binary_subj_383   B    FALSE #> 384 pseudo_binary_subj_384   B    FALSE #> 385 pseudo_binary_subj_385   B    FALSE #> 386 pseudo_binary_subj_386   B    FALSE #> 387 pseudo_binary_subj_387   B    FALSE #> 388 pseudo_binary_subj_388   B    FALSE #> 389 pseudo_binary_subj_389   B    FALSE #> 390 pseudo_binary_subj_390   B    FALSE #> 391 pseudo_binary_subj_391   B    FALSE #> 392 pseudo_binary_subj_392   B    FALSE #> 393 pseudo_binary_subj_393   B    FALSE #> 394 pseudo_binary_subj_394   B    FALSE #> 395 pseudo_binary_subj_395   B    FALSE #> 396 pseudo_binary_subj_396   B    FALSE #> 397 pseudo_binary_subj_397   B    FALSE #> 398 pseudo_binary_subj_398   B    FALSE #> 399 pseudo_binary_subj_399   B    FALSE #> 400 pseudo_binary_subj_400   B    FALSE"},{"path":"https://hta-pharma.github.io/maicplus/reference/get_time_as.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert Time Values Using Scaling Factors — get_time_as","title":"Convert Time Values Using Scaling Factors — get_time_as","text":"Convert Time Values Using Scaling Factors","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/get_time_as.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert Time Values Using Scaling Factors — get_time_as","text":"","code":"get_time_as(times, as = c(\"days\", \"weeks\", \"months\", \"years\"))"},{"path":"https://hta-pharma.github.io/maicplus/reference/get_time_as.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert Time Values Using Scaling Factors — get_time_as","text":"times Numeric time values time scale convert ","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/get_time_as.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert Time Values Using Scaling Factors — get_time_as","text":"Returns numeric vector calculated times / get_time_conversion(factor = )","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/kmplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","title":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","text":"wrapper function basic_kmplot. argument setting similar maic_anchored maic_unanchored, used two functions.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/kmplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","text":"","code":"kmplot(   weights_object,   tte_ipd,   tte_pseudo_ipd,   trt_ipd,   trt_agd,   trt_common = NULL,   trt_var_ipd = \"ARM\",   trt_var_agd = \"ARM\",   km_conf_type = \"log-log\",   km_layout = c(\"all\", \"by_trial\", \"by_arm\"),   ... )"},{"path":"https://hta-pharma.github.io/maicplus/reference/kmplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","text":"weights_object object returned estimate_weight tte_ipd data frame individual patient data (IPD) internal trial, contain least \"USUBJID\", \"EVENT\", \"TIME\" columns column indicating treatment assignment tte_pseudo_ipd data frame pseudo IPD digitized KM curves external trial (time--event endpoint), contain least \"EVENT\", \"TIME\" trt_ipd string, name interested investigation arm internal trial dat_igd (real IPD) trt_agd string, name interested investigation arm external trial dat_pseudo (pseudo IPD) trt_common string, name common comparator internal external trial, default NULL, indicating unanchored case trt_var_ipd string, column name tte_ipd contains treatment assignment trt_var_agd string, column name tte_pseudo_ipd contains treatment assignment km_conf_type string, pass conf.type survfit km_layout string, applicable unanchored case (trt_common = NULL), indicated desired layout output KM curve. ... arguments basic_kmplot","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/kmplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","text":"unanchored case, KM plot risk set table. anchored case, depending km_layout, \"by_trial\", 2 1 plot, first KM curves (incl. weighted) IPD trial, KM curves AgD trial, risk set table. \"by_arm\", 2 1 plot, first KM curves trt_agd  trt_ipd (without weights), KM curves trt_common AgD trial IPD trial (without weights). Risk set table appended. \"\", 2 2 plot, plots \"by_trial\" \"by_arm\" without risk set table appended.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/kmplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kaplan Meier (KM) plot function for anchored and unanchored cases — kmplot","text":"","code":"# anchored example using kmplot  ### IPD # Read in relevant ADaM data and rename variables of interest adsl_twt #>      USUBJID ARM AGE    SEX SMOKE ECOG0 N_PR_THER SEX_MALE #> 1        xx1   A  45   Male     0     0         4        1 #> 2        xx2   A  71   Male     0     0         3        1 #> 3        xx3   A  58   Male     1     1         2        1 #> 4        xx4   A  48 Female     0     1         4        0 #> 5        xx5   A  69   Male     0     1         4        1 #> 6        xx6   A  48 Female     0     1         4        0 #> 7        xx7   A  47   Male     1     0         3        1 #> 8        xx8   A  61   Male     1     0         1        1 #> 9        xx9   A  54 Female     1     1         1        0 #> 10      xx10   A  56 Female     1     0         3        0 #> 11      xx11   A  63 Female     0     0         4        0 #> 12      xx12   A  50 Female     0     0         1        0 #> 13      xx13   A  57   Male     0     1         3        1 #> 14      xx14   A  62 Female     1     1         1        0 #> 15      xx15   A  57 Female     0     1         3        0 #> 16      xx16   A  66   Male     0     0         2        1 #> 17      xx17   A  75   Male     1     1         3        1 #> 18      xx18   A  47 Female     0     0         4        0 #> 19      xx19   A  57   Male     0     0         3        1 #> 20      xx20   A  54   Male     0     0         3        1 #> 21      xx21   A  55   Male     1     0         3        1 #> 22      xx22   A  64   Male     0     1         3        1 #> 23      xx23   A  53 Female     1     0         3        0 #> 24      xx24   A  58   Male     1     1         2        1 #> 25      xx25   A  47   Male     0     0         1        1 #> 26      xx26   A  60 Female     1     0         1        0 #> 27      xx27   A  49 Female     0     1         3        0 #> 28      xx28   A  55 Female     0     0         1        0 #> 29      xx29   A  66 Female     0     1         2        0 #> 30      xx30   A  58   Male     0     1         4        1 #> 31      xx31   A  49   Male     0     1         4        1 #> 32      xx32   A  61   Male     0     0         4        1 #> 33      xx33   A  66   Male     1     0         3        1 #> 34      xx34   A  45   Male     0     0         1        1 #> 35      xx35   A  59 Female     1     1         2        0 #> 36      xx36   A  74 Female     1     0         4        0 #> 37      xx37   A  73 Female     0     0         3        0 #> 38      xx38   A  74   Male     0     1         4        1 #> 39      xx39   A  54   Male     0     0         1        1 #> 40      xx40   A  58 Female     1     1         1        0 #> 41      xx41   A  61 Female     0     1         3        0 #> 42      xx42   A  47 Female     1     1         2        0 #> 43      xx43   A  73 Female     1     1         2        0 #> 44      xx44   A  68   Male     0     0         1        1 #> 45      xx45   A  49 Female     0     0         3        0 #> 46      xx46   A  71 Female     0     0         2        0 #> 47      xx47   A  70   Male     0     1         4        1 #> 48      xx48   A  62 Female     1     0         1        0 #> 49      xx49   A  49   Male     0     0         1        1 #> 50      xx50   A  74 Female     0     0         1        0 #> 51      xx51   A  46 Female     0     1         3        0 #> 52      xx52   A  68 Female     1     0         3        0 #> 53      xx53   A  46   Male     1     0         2        1 #> 54      xx54   A  75 Female     1     1         3        0 #> 55      xx55   A  47 Female     0     0         3        0 #> 56      xx56   A  56   Male     0     1         3        1 #> 57      xx57   A  72 Female     0     0         3        0 #> 58      xx58   A  57   Male     1     1         4        1 #> 59      xx59   A  46   Male     0     0         1        1 #> 60      xx60   A  56 Female     1     1         1        0 #> 61      xx61   A  73   Male     0     1         2        1 #> 62      xx62   A  60 Female     1     1         3        0 #> 63      xx63   A  75   Male     0     0         2        1 #> 64      xx64   A  69 Female     1     1         2        0 #> 65      xx65   A  47 Female     0     1         1        0 #> 66      xx66   A  74   Male     0     0         4        1 #> 67      xx67   A  71 Female     0     1         1        0 #> 68      xx68   A  49 Female     1     1         1        0 #> 69      xx69   A  68   Male     0     0         3        1 #> 70      xx70   A  49   Male     0     1         1        1 #> 71      xx71   A  70   Male     0     1         1        1 #> 72      xx72   A  45 Female     0     0         2        0 #> 73      xx73   A  47 Female     0     1         3        0 #> 74      xx74   A  58   Male     0     1         3        1 #> 75      xx75   A  49 Female     0     1         4        0 #> 76      xx76   A  68 Female     0     0         1        0 #> 77      xx77   A  60   Male     0     0         4        1 #> 78      xx78   A  45 Female     1     0         1        0 #> 79      xx79   A  57 Female     0     0         1        0 #> 80      xx80   A  50 Female     0     1         1        0 #> 81      xx81   A  63   Male     0     1         3        1 #> 82      xx82   A  47 Female     0     0         2        0 #> 83      xx83   A  68 Female     0     1         4        0 #> 84      xx84   A  51   Male     0     0         4        1 #> 85      xx85   A  60   Male     0     0         1        1 #> 86      xx86   A  52 Female     1     0         4        0 #> 87      xx87   A  69   Male     1     1         1        1 #> 88      xx88   A  70 Female     0     1         4        0 #> 89      xx89   A  72   Male     0     0         2        1 #> 90      xx90   A  46 Female     0     1         1        0 #> 91      xx91   A  51   Male     1     0         4        1 #> 92      xx92   A  69 Female     0     1         1        0 #> 93      xx93   A  66   Male     0     0         1        1 #> 94      xx94   A  73   Male     0     1         1        1 #> 95      xx95   A  73 Female     1     0         3        0 #> 96      xx96   A  62 Female     0     1         2        0 #> 97      xx97   A  55 Female     0     0         4        0 #> 98      xx98   A  67   Male     1     0         3        1 #> 99      xx99   A  54 Female     1     0         3        0 #> 100    xx100   A  52 Female     1     1         4        0 #> 101    xx101   A  57   Male     0     0         2        1 #> 102    xx102   A  57 Female     1     1         3        0 #> 103    xx103   A  57   Male     0     0         3        1 #> 104    xx104   A  67 Female     1     1         2        0 #> 105    xx105   A  67 Female     1     1         2        0 #> 106    xx106   A  74 Female     1     1         2        0 #> 107    xx107   A  72 Female     1     0         2        0 #> 108    xx108   A  73 Female     0     0         3        0 #> 109    xx109   A  57 Female     0     0         4        0 #> 110    xx110   A  69 Female     1     0         1        0 #> 111    xx111   A  55   Male     0     0         1        1 #> 112    xx112   A  74 Female     0     0         4        0 #> 113    xx113   A  68 Female     0     0         4        0 #> 114    xx114   A  53   Male     0     0         2        1 #> 115    xx115   A  69   Male     0     0         2        1 #> 116    xx116   A  68   Male     0     1         2        1 #> 117    xx117   A  58   Male     0     0         1        1 #> 118    xx118   A  64 Female     0     0         3        0 #> 119    xx119   A  71   Male     0     0         1        1 #> 120    xx120   A  69 Female     0     1         2        0 #> 121    xx121   A  64 Female     1     0         4        0 #> 122    xx122   A  64   Male     1     0         1        1 #> 123    xx123   A  55   Male     0     1         3        1 #> 124    xx124   A  74   Male     0     0         3        1 #> 125    xx125   A  50   Male     0     1         3        1 #> 126    xx126   A  68   Male     0     1         1        1 #> 127    xx127   A  60   Male     0     0         2        1 #> 128    xx128   A  59 Female     0     0         3        0 #> 129    xx129   A  71 Female     1     0         3        0 #> 130    xx130   A  69   Male     0     1         4        1 #> 131    xx131   A  56 Female     0     1         1        0 #> 132    xx132   A  51   Male     0     1         3        1 #> 133    xx133   A  65   Male     0     1         2        1 #> 134    xx134   A  45   Male     1     1         3        1 #> 135    xx135   A  49 Female     0     0         2        0 #> 136    xx136   A  74 Female     1     0         4        0 #> 137    xx137   A  71 Female     1     1         3        0 #> 138    xx138   A  65   Male     0     0         1        1 #> 139    xx139   A  67 Female     0     0         2        0 #> 140    xx140   A  48 Female     0     0         3        0 #> 141    xx141   A  70 Female     0     1         4        0 #> 142    xx142   A  72 Female     0     0         1        0 #> 143    xx143   A  53   Male     1     1         4        1 #> 144    xx144   A  68 Female     1     0         3        0 #> 145    xx145   A  65   Male     0     1         1        1 #> 146    xx146   A  70 Female     1     1         4        0 #> 147    xx147   A  58   Male     0     0         2        1 #> 148    xx148   A  68 Female     0     1         4        0 #> 149    xx149   A  54 Female     0     0         4        0 #> 150    xx150   A  75 Female     1     1         4        0 #> 151    xx151   A  49 Female     1     1         3        0 #> 152    xx152   A  60   Male     0     0         4        1 #> 153    xx153   A  45 Female     1     0         4        0 #> 154    xx154   A  73 Female     1     0         3        0 #> 155    xx155   A  71 Female     0     1         3        0 #> 156    xx156   A  73 Female     0     0         3        0 #> 157    xx157   A  58 Female     0     0         4        0 #> 158    xx158   A  59 Female     0     1         2        0 #> 159    xx159   A  75 Female     0     1         2        0 #> 160    xx160   A  65   Male     0     0         2        1 #> 161    xx161   A  48   Male     1     0         2        1 #> 162    xx162   A  63 Female     0     1         2        0 #> 163    xx163   A  74 Female     0     0         3        0 #> 164    xx164   A  64 Female     0     0         3        0 #> 165    xx165   A  49 Female     0     0         1        0 #> 166    xx166   A  56 Female     0     1         4        0 #> 167    xx167   A  56 Female     0     1         2        0 #> 168    xx168   A  48 Female     0     0         1        0 #> 169    xx169   A  65   Male     0     1         3        1 #> 170    xx170   A  53 Female     0     0         3        0 #> 171    xx171   A  72   Male     0     0         2        1 #> 172    xx172   A  75 Female     1     0         4        0 #> 173    xx173   A  70 Female     0     1         2        0 #> 174    xx174   A  53 Female     1     0         2        0 #> 175    xx175   A  45 Female     1     1         3        0 #> 176    xx176   A  53 Female     1     1         3        0 #> 177    xx177   A  52 Female     0     1         2        0 #> 178    xx178   A  61 Female     1     0         4        0 #> 179    xx179   A  70   Male     0     0         3        1 #> 180    xx180   A  58 Female     0     0         3        0 #> 181    xx181   A  54   Male     0     0         1        1 #> 182    xx182   A  53   Male     0     0         2        1 #> 183    xx183   A  74   Male     0     0         2        1 #> 184    xx184   A  64   Male     0     1         3        1 #> 185    xx185   A  52   Male     0     0         1        1 #> 186    xx186   A  73 Female     0     0         3        0 #> 187    xx187   A  55 Female     1     0         4        0 #> 188    xx188   A  71 Female     0     0         3        0 #> 189    xx189   A  57 Female     1     0         3        0 #> 190    xx190   A  49 Female     1     0         2        0 #> 191    xx191   A  69   Male     0     0         4        1 #> 192    xx192   A  74 Female     1     0         2        0 #> 193    xx193   A  59 Female     0     0         3        0 #> 194    xx194   A  53   Male     0     0         4        1 #> 195    xx195   A  52 Female     0     1         3        0 #> 196    xx196   A  47 Female     1     1         1        0 #> 197    xx197   A  61 Female     0     0         4        0 #> 198    xx198   A  51 Female     0     0         2        0 #> 199    xx199   A  62 Female     1     0         1        0 #> 200    xx200   A  59 Female     1     0         2        0 #> 201    xx201   A  58   Male     0     0         2        1 #> 202    xx202   A  61 Female     0     1         4        0 #> 203    xx203   A  45 Female     0     0         1        0 #> 204    xx204   A  59   Male     1     1         2        1 #> 205    xx205   A  58 Female     1     0         2        0 #> 206    xx206   A  67   Male     0     0         1        1 #> 207    xx207   A  51 Female     1     0         2        0 #> 208    xx208   A  68   Male     1     1         3        1 #> 209    xx209   A  53 Female     1     0         2        0 #> 210    xx210   A  64   Male     1     0         3        1 #> 211    xx211   A  61   Male     0     1         1        1 #> 212    xx212   A  52   Male     1     0         2        1 #> 213    xx213   A  57 Female     0     1         4        0 #> 214    xx214   A  57 Female     1     0         2        0 #> 215    xx215   A  49 Female     0     0         4        0 #> 216    xx216   A  51   Male     1     1         3        1 #> 217    xx217   A  60 Female     0     0         2        0 #> 218    xx218   A  60 Female     1     0         1        0 #> 219    xx219   A  71 Female     0     0         3        0 #> 220    xx220   A  54 Female     0     0         1        0 #> 221    xx221   A  51   Male     1     0         1        1 #> 222    xx222   A  71   Male     0     1         3        1 #> 223    xx223   A  47   Male     0     0         2        1 #> 224    xx224   A  65   Male     0     0         1        1 #> 225    xx225   A  53 Female     0     0         1        0 #> 226    xx226   A  56 Female     0     0         1        0 #> 227    xx227   A  51   Male     1     0         4        1 #> 228    xx228   A  68 Female     0     0         2        0 #> 229    xx229   A  75 Female     0     1         1        0 #> 230    xx230   A  49 Female     0     0         2        0 #> 231    xx231   A  74   Male     1     0         1        1 #> 232    xx232   A  66 Female     0     0         1        0 #> 233    xx233   A  74 Female     0     0         1        0 #> 234    xx234   A  62 Female     0     1         2        0 #> 235    xx235   A  53 Female     0     0         1        0 #> 236    xx236   A  62 Female     0     1         1        0 #> 237    xx237   A  70 Female     1     0         3        0 #> 238    xx238   A  60 Female     1     0         2        0 #> 239    xx239   A  72   Male     0     0         1        1 #> 240    xx240   A  74 Female     1     0         3        0 #> 241    xx241   A  47 Female     1     0         3        0 #> 242    xx242   A  54 Female     0     1         3        0 #> 243    xx243   A  65 Female     0     1         2        0 #> 244    xx244   A  74   Male     0     1         1        1 #> 245    xx245   A  61   Male     0     1         2        1 #> 246    xx246   A  54 Female     1     0         3        0 #> 247    xx247   A  65   Male     0     0         3        1 #> 248    xx248   A  72 Female     1     0         3        0 #> 249    xx249   A  71 Female     1     1         2        0 #> 250    xx250   A  65   Male     0     0         3        1 #> 251    xx251   A  58   Male     0     1         1        1 #> 252    xx252   A  46   Male     0     0         1        1 #> 253    xx253   A  53 Female     1     0         4        0 #> 254    xx254   A  71 Female     1     0         4        0 #> 255    xx255   A  47   Male     0     1         3        1 #> 256    xx256   A  45   Male     0     0         1        1 #> 257    xx257   A  50   Male     0     0         3        1 #> 258    xx258   A  67 Female     1     0         3        0 #> 259    xx259   A  72   Male     1     0         2        1 #> 260    xx260   A  45 Female     0     1         2        0 #> 261    xx261   A  75 Female     1     0         1        0 #> 262    xx262   A  65   Male     0     0         3        1 #> 263    xx263   A  60 Female     0     1         3        0 #> 264    xx264   A  75 Female     0     1         4        0 #> 265    xx265   A  60 Female     1     0         4        0 #> 266    xx266   A  49 Female     1     0         2        0 #> 267    xx267   A  58 Female     0     1         1        0 #> 268    xx268   A  57   Male     1     1         3        1 #> 269    xx269   A  69   Male     1     0         2        1 #> 270    xx270   A  51   Male     0     1         1        1 #> 271    xx271   A  54 Female     1     0         4        0 #> 272    xx272   A  55   Male     0     1         3        1 #> 273    xx273   A  49 Female     0     0         4        0 #> 274    xx274   A  74 Female     1     0         1        0 #> 275    xx275   A  55   Male     1     0         1        1 #> 276    xx276   A  52 Female     1     0         1        0 #> 277    xx277   A  65   Male     0     0         2        1 #> 278    xx278   A  70 Female     1     0         1        0 #> 279    xx279   A  66 Female     1     1         2        0 #> 280    xx280   A  63 Female     0     1         4        0 #> 281    xx281   A  61 Female     0     1         3        0 #> 282    xx282   A  65   Male     0     1         2        1 #> 283    xx283   A  73   Male     0     0         2        1 #> 284    xx284   A  55 Female     1     1         4        0 #> 285    xx285   A  56 Female     1     1         4        0 #> 286    xx286   A  68 Female     0     1         1        0 #> 287    xx287   A  74 Female     1     0         4        0 #> 288    xx288   A  67 Female     0     0         2        0 #> 289    xx289   A  66   Male     0     1         3        1 #> 290    xx290   A  48 Female     0     0         3        0 #> 291    xx291   A  49 Female     1     1         3        0 #> 292    xx292   A  60 Female     1     1         1        0 #> 293    xx293   A  69 Female     0     1         4        0 #> 294    xx294   A  58 Female     0     1         3        0 #> 295    xx295   A  45 Female     1     1         4        0 #> 296    xx296   A  49 Female     0     1         1        0 #> 297    xx297   A  67 Female     1     0         4        0 #> 298    xx298   A  63   Male     0     0         4        1 #> 299    xx299   A  50 Female     0     0         2        0 #> 300    xx300   A  68 Female     0     1         3        0 #> 301    xx301   A  53   Male     0     1         2        1 #> 302    xx302   A  63   Male     0     1         2        1 #> 303    xx303   A  58   Male     0     0         4        1 #> 304    xx304   A  70 Female     0     1         4        0 #> 305    xx305   A  56 Female     0     0         1        0 #> 306    xx306   A  56   Male     0     1         3        1 #> 307    xx307   A  61 Female     0     0         3        0 #> 308    xx308   A  72   Male     0     0         2        1 #> 309    xx309   A  51   Male     0     1         1        1 #> 310    xx310   A  72   Male     0     0         4        1 #> 311    xx311   A  64 Female     0     0         3        0 #> 312    xx312   A  59   Male     0     0         2        1 #> 313    xx313   A  75 Female     0     0         3        0 #> 314    xx314   A  75 Female     0     0         1        0 #> 315    xx315   A  74   Male     0     0         2        1 #> 316    xx316   A  54   Male     0     1         4        1 #> 317    xx317   A  55 Female     0     0         3        0 #> 318    xx318   A  52 Female     1     0         1        0 #> 319    xx319   A  46 Female     0     1         1        0 #> 320    xx320   A  53   Male     0     0         1        1 #> 321    xx321   A  54 Female     0     1         1        0 #> 322    xx322   A  62 Female     0     0         2        0 #> 323    xx323   A  54   Male     1     0         4        1 #> 324    xx324   A  56 Female     0     0         4        0 #> 325    xx325   A  48 Female     0     0         1        0 #> 326    xx326   A  52 Female     0     1         1        0 #> 327    xx327   A  55 Female     0     1         3        0 #> 328    xx328   A  69 Female     0     0         2        0 #> 329    xx329   A  48 Female     0     0         1        0 #> 330    xx330   A  48 Female     1     1         3        0 #> 331    xx331   A  60   Male     0     0         3        1 #> 332    xx332   A  74 Female     0     1         2        0 #> 333    xx333   A  45 Female     0     0         1        0 #> 334    xx334   A  64   Male     1     0         1        1 #> 335    xx335   A  75 Female     1     1         3        0 #> 336    xx336   A  62 Female     0     0         3        0 #> 337    xx337   A  71   Male     0     0         4        1 #> 338    xx338   A  48 Female     1     1         3        0 #> 339    xx339   A  53 Female     0     0         2        0 #> 340    xx340   A  62   Male     0     0         3        1 #> 341    xx341   A  69 Female     0     0         2        0 #> 342    xx342   A  72 Female     0     0         1        0 #> 343    xx343   A  61 Female     0     0         2        0 #> 344    xx344   A  47   Male     0     0         2        1 #> 345    xx345   A  58   Male     0     1         1        1 #> 346    xx346   A  52 Female     0     0         4        0 #> 347    xx347   A  49 Female     0     0         4        0 #> 348    xx348   A  51 Female     0     0         1        0 #> 349    xx349   A  51 Female     1     1         2        0 #> 350    xx350   A  72   Male     0     0         3        1 #> 351    xx351   A  68   Male     0     0         3        1 #> 352    xx352   A  49 Female     0     0         4        0 #> 353    xx353   A  45 Female     0     0         3        0 #> 354    xx354   A  49 Female     0     0         3        0 #> 355    xx355   A  65   Male     0     1         2        1 #> 356    xx356   A  56   Male     0     0         2        1 #> 357    xx357   A  45 Female     1     1         1        0 #> 358    xx358   A  57   Male     1     0         2        1 #> 359    xx359   A  53   Male     1     1         2        1 #> 360    xx360   A  65 Female     0     0         2        0 #> 361    xx361   A  57   Male     0     1         4        1 #> 362    xx362   A  55 Female     0     1         4        0 #> 363    xx363   A  57   Male     0     1         2        1 #> 364    xx364   A  46 Female     0     1         4        0 #> 365    xx365   A  69 Female     0     1         1        0 #> 366    xx366   A  67 Female     0     1         3        0 #> 367    xx367   A  55   Male     0     0         1        1 #> 368    xx368   A  53 Female     0     1         3        0 #> 369    xx369   A  46 Female     0     0         3        0 #> 370    xx370   A  71   Male     0     0         4        1 #> 371    xx371   A  68   Male     0     1         1        1 #> 372    xx372   A  49 Female     0     1         3        0 #> 373    xx373   A  51 Female     0     0         3        0 #> 374    xx374   A  65 Female     1     1         3        0 #> 375    xx375   A  55 Female     0     0         4        0 #> 376    xx376   A  53   Male     0     0         4        1 #> 377    xx377   A  65 Female     0     1         4        0 #> 378    xx378   A  72 Female     1     1         4        0 #> 379    xx379   A  61   Male     0     1         1        1 #> 380    xx380   A  73 Female     0     0         1        0 #> 381    xx381   A  62 Female     1     1         2        0 #> 382    xx382   A  46   Male     0     0         2        1 #> 383    xx383   A  51   Male     0     1         4        1 #> 384    xx384   A  60   Male     1     1         4        1 #> 385    xx385   A  56 Female     0     1         3        0 #> 386    xx386   A  69 Female     0     1         1        0 #> 387    xx387   A  58 Female     1     1         2        0 #> 388    xx388   A  58 Female     1     1         3        0 #> 389    xx389   A  53 Female     0     1         2        0 #> 390    xx390   A  47 Female     0     0         2        0 #> 391    xx391   A  59   Male     0     1         2        1 #> 392    xx392   A  47 Female     0     0         4        0 #> 393    xx393   A  60 Female     1     0         4        0 #> 394    xx394   A  73 Female     0     0         4        0 #> 395    xx395   A  60   Male     0     0         1        1 #> 396    xx396   A  75   Male     0     0         4        1 #> 397    xx397   A  65 Female     0     0         3        0 #> 398    xx398   A  68   Male     0     0         1        1 #> 399    xx399   A  55 Female     0     0         4        0 #> 400    xx400   A  46 Female     1     0         4        0 #> 401    xx401   A  45 Female     0     1         1        0 #> 402    xx402   A  70   Male     1     1         3        1 #> 403    xx403   A  56 Female     0     1         4        0 #> 404    xx404   A  62 Female     0     0         3        0 #> 405    xx405   A  49   Male     0     0         4        1 #> 406    xx406   A  52 Female     0     0         4        0 #> 407    xx407   A  67 Female     0     1         4        0 #> 408    xx408   A  50 Female     1     1         1        0 #> 409    xx409   A  68 Female     1     1         2        0 #> 410    xx410   A  54 Female     0     0         4        0 #> 411    xx411   A  65   Male     0     0         4        1 #> 412    xx412   A  55 Female     0     1         2        0 #> 413    xx413   A  53 Female     1     1         4        0 #> 414    xx414   A  71 Female     0     0         2        0 #> 415    xx415   A  48   Male     0     0         1        1 #> 416    xx416   A  54 Female     0     1         3        0 #> 417    xx417   A  75   Male     1     0         3        1 #> 418    xx418   A  53 Female     0     0         2        0 #> 419    xx419   A  50 Female     1     0         4        0 #> 420    xx420   A  64 Female     1     0         1        0 #> 421    xx421   A  65 Female     0     0         2        0 #> 422    xx422   A  65 Female     0     1         4        0 #> 423    xx423   A  60 Female     1     0         2        0 #> 424    xx424   A  70 Female     1     0         3        0 #> 425    xx425   A  51 Female     0     0         2        0 #> 426    xx426   A  45 Female     0     1         1        0 #> 427    xx427   A  75 Female     1     0         2        0 #> 428    xx428   A  52 Female     1     0         1        0 #> 429    xx429   A  70   Male     0     0         4        1 #> 430    xx430   A  69 Female     1     1         3        0 #> 431    xx431   A  64 Female     0     0         2        0 #> 432    xx432   A  68 Female     1     0         1        0 #> 433    xx433   A  51   Male     1     0         1        1 #> 434    xx434   A  59 Female     0     1         2        0 #> 435    xx435   A  57 Female     0     0         1        0 #> 436    xx436   A  47   Male     0     0         2        1 #> 437    xx437   A  65   Male     0     1         1        1 #> 438    xx438   A  65   Male     0     1         1        1 #> 439    xx439   A  65   Male     0     0         2        1 #> 440    xx440   A  46   Male     0     0         2        1 #> 441    xx441   A  64 Female     0     0         3        0 #> 442    xx442   A  57 Female     0     1         4        0 #> 443    xx443   A  67 Female     0     1         3        0 #> 444    xx444   A  61 Female     1     0         3        0 #> 445    xx445   A  56   Male     0     0         4        1 #> 446    xx446   A  52   Male     0     0         3        1 #> 447    xx447   A  74 Female     1     1         3        0 #> 448    xx448   A  75   Male     0     1         3        1 #> 449    xx449   A  58   Male     0     1         3        1 #> 450    xx450   A  57 Female     0     1         4        0 #> 451    xx451   A  55 Female     0     0         1        0 #> 452    xx452   A  53 Female     1     0         1        0 #> 453    xx453   A  75   Male     1     0         4        1 #> 454    xx454   A  65 Female     0     1         3        0 #> 455    xx455   A  65 Female     0     0         4        0 #> 456    xx456   A  58   Male     0     0         4        1 #> 457    xx457   A  71 Female     0     0         3        0 #> 458    xx458   A  71   Male     0     0         4        1 #> 459    xx459   A  59   Male     1     0         4        1 #> 460    xx460   A  46   Male     1     0         4        1 #> 461    xx461   A  51 Female     0     0         4        0 #> 462    xx462   A  56 Female     0     1         4        0 #> 463    xx463   A  66 Female     0     0         1        0 #> 464    xx464   A  59 Female     1     0         2        0 #> 465    xx465   A  48 Female     1     0         2        0 #> 466    xx466   A  68 Female     0     0         4        0 #> 467    xx467   A  57 Female     0     0         4        0 #> 468    xx468   A  63   Male     0     0         4        1 #> 469    xx469   A  62   Male     0     1         3        1 #> 470    xx470   A  70 Female     0     0         3        0 #> 471    xx471   A  55 Female     0     1         2        0 #> 472    xx472   A  56   Male     1     0         3        1 #> 473    xx473   A  51   Male     1     0         1        1 #> 474    xx474   A  51 Female     0     0         4        0 #> 475    xx475   A  46 Female     1     1         3        0 #> 476    xx476   A  52   Male     0     0         4        1 #> 477    xx477   A  71   Male     0     1         4        1 #> 478    xx478   A  54   Male     1     0         3        1 #> 479    xx479   A  55   Male     0     1         2        1 #> 480    xx480   A  46 Female     0     0         3        0 #> 481    xx481   A  70 Female     0     0         1        0 #> 482    xx482   A  68 Female     0     1         3        0 #> 483    xx483   A  50   Male     0     1         1        1 #> 484    xx484   A  45 Female     0     0         2        0 #> 485    xx485   A  68   Male     0     0         1        1 #> 486    xx486   A  56   Male     0     1         2        1 #> 487    xx487   A  59   Male     0     1         3        1 #> 488    xx488   A  51   Male     1     1         1        1 #> 489    xx489   A  61 Female     1     0         3        0 #> 490    xx490   A  60 Female     0     1         3        0 #> 491    xx491   A  68 Female     1     0         2        0 #> 492    xx492   A  67   Male     1     1         4        1 #> 493    xx493   A  45   Male     1     1         2        1 #> 494    xx494   A  71 Female     0     1         2        0 #> 495    xx495   A  55   Male     0     1         3        1 #> 496    xx496   A  72 Female     0     0         4        0 #> 497    xx497   A  48 Female     1     0         3        0 #> 498    xx498   A  68 Female     1     0         1        0 #> 499    xx499   A  45 Female     0     0         1        0 #> 500    xx500   A  58 Female     1     1         3        0 #> 1721   yyxx1   C  75 Female     1     0         4        0 #> 3651  yyxx10   C  69 Female     0     1         1        0 #> 4881 yyxx100   C  51   Male     1     1         1        1 #> 2281 yyxx101   C  68 Female     0     0         2        0 #> 4231 yyxx102   C  60 Female     1     0         2        0 #> 1981 yyxx103   C  51 Female     0     0         2        0 #> 2401 yyxx104   C  74 Female     1     0         3        0 #> 910  yyxx105   C  54 Female     1     1  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Female     1     0         2        0 #> 4591  yyxx18   C  59   Male     1     0         4        1 #> 3791 yyxx180   C  61   Male     0     1         1        1 #> 2851 yyxx181   C  56 Female     1     1         4        0 #> 4941 yyxx182   C  71 Female     0     1         2        0 #> 4071 yyxx183   C  67 Female     0     1         4        0 #> 3291 yyxx184   C  48 Female     0     0         1        0 #> 1631 yyxx185   C  74 Female     0     0         3        0 #> 711  yyxx186   C  70   Male     0     1         1        1 #> 1301 yyxx187   C  69   Male     0     1         4        1 #> 4100 yyxx188   C  48 Female     0     1         4        0 #> 841  yyxx189   C  51   Male     0     0         4        1 #> 2321  yyxx19   C  66 Female     0     0         1        0 #> 1081 yyxx190   C  73 Female     0     0         3        0 #> 4041 yyxx191   C  62 Female     0     0         3        0 #> 4661 yyxx192   C  68 Female     0     0         4        0 #> 631  yyxx193   C  75   Male   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4731 yyxx251   C  51   Male     1     0         1        1 #> 1710 yyxx252   C  75   Male     1     1         3        1 #> 1201 yyxx253   C  69 Female     0     1         2        0 #> 1501 yyxx254   C  75 Female     1     1         4        0 #> 510  yyxx255   C  69   Male     0     1         4        1 #> 3361 yyxx256   C  62 Female     0     0         3        0 #> 3661 yyxx257   C  67 Female     0     1         3        0 #> 1371 yyxx258   C  71 Female     1     1         3        0 #> 851  yyxx259   C  60   Male     0     0         1        1 #> 2010  yyxx26   C  54   Male     0     0         3        1 #> 4741 yyxx260   C  51 Female     0     0         4        0 #> 751  yyxx261   C  49 Female     0     1         4        0 #> 4261 yyxx262   C  45 Female     0     1         1        0 #> 2841 yyxx263   C  55 Female     1     1         4        0 #> 1671 yyxx264   C  56 Female     0     1         2        0 #> 2021 yyxx265   C  61 Female     0     1         4        0 #> 1251 yyxx266   C  50   Male     0     1         3        1 #> 3691 yyxx267   C  46 Female     0     0         3        0 #> 4711 yyxx268   C  55 Female     0     1         2        0 #> 1561 yyxx269   C  73 Female     0     0         3        0 #> 4851  yyxx27   C  68   Male     0     0         1        1 #> 1211 yyxx270   C  64 Female     1     0         4        0 #> 4461 yyxx271   C  52   Male     0     0         3        1 #> 1410 yyxx272   C  62 Female     1     1         1        0 #> 3811 yyxx273   C  62 Female     1     1         2        0 #> 4191 yyxx274   C  50 Female     1     0         4        0 #> 1831 yyxx275   C  74   Male     0     0         2        1 #> 4010 yyxx276   C  58 Female     1     1         1        0 #> 1471 yyxx277   C  58   Male     0     0         2        1 #> 1531 yyxx278   C  45 Female     1     0         4        0 #> 2751 yyxx279   C  55   Male     1     0         1        1 #> 3461  yyxx28   C  52 Female     0     0         4        0 #> 4761 yyxx280  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Female     0     0         3        0 #> 1651 yyxx296   C  49 Female     0     0         1        0 #> 3711 yyxx297   C  68   Male     0     1         1        1 #> 2421 yyxx298   C  54 Female     0     1         3        0 #> 4451 yyxx299   C  56   Male     0     0         4        1 #> 4321   yyxx3   C  68 Female     1     0         1        0 #> 3541  yyxx30   C  49 Female     0     0         3        0 #> 4431 yyxx300   C  67 Female     0     1         3        0 #> 3501 yyxx301   C  72   Male     0     0         3        1 #> 3841 yyxx302   C  60   Male     1     1         4        1 #> 2771 yyxx303   C  65   Male     0     0         2        1 #> 3310 yyxx304   C  66   Male     1     0         3        1 #> 571  yyxx305   C  72 Female     0     0         3        0 #> 1621 yyxx306   C  63 Female     0     1         2        0 #> 2741 yyxx307   C  74 Female     1     0         1        0 #> 2211 yyxx308   C  51   Male     1     0         1        1 #> 3991 yyxx309   C  55 Female   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Overall Survival    1  59.36676287 #> 68      xx68   A 500.0000000  DAYS      OS Overall Survival    1 500.00000000 #> 69      xx69   A 101.2849073  DAYS      OS Overall Survival    1 101.28490730 #> 70      xx70   A 500.0000000  DAYS      OS Overall Survival    1 500.00000000 #> 71      xx71   A  12.7575511  DAYS      OS Overall Survival    0  12.75755111 #> 72      xx72   A 170.4704706  DAYS      OS Overall Survival    1 170.47047063 #> 73      xx73   A 379.0575377  DAYS      OS Overall Survival    1 379.05753770 #> 74      xx74   A 295.6642687  DAYS      OS Overall Survival    1 295.66426874 #> 75      xx75   A 180.0471671  DAYS      OS Overall Survival    0 180.04716714 #> 76      xx76   A  88.3276166  DAYS      OS Overall Survival    1  88.32761664 #> 77      xx77   A 230.9483853  DAYS      OS Overall Survival    0 230.94838529 #> 78      xx78   A  19.5843537  DAYS      OS Overall Survival    1  19.58435366 #> 79      xx79   A  67.6855902  DAYS      OS Overall Survival    1  67.68559019 #> 80      xx80   A 187.2930109  DAYS      OS Overall Survival    0 187.29301095 #> 81      xx81   A 205.1596244  DAYS      OS Overall Survival    1 205.15962436 #> 82      xx82   A 133.9676829  DAYS      OS Overall Survival    1 133.96768294 #> 83      xx83   A  99.3645367  DAYS      OS Overall Survival    1  99.36453671 #> 84      xx84   A 152.3036359  DAYS      OS Overall Survival    0 152.30363591 #> 85      xx85   A 158.5194710  DAYS      OS Overall Survival    0 158.51947100 #> 86      xx86   A 203.4020672  DAYS      OS Overall Survival    0 203.40206722 #> 87      xx87   A 336.8420868  DAYS      OS Overall Survival    0 336.84208675 #> 88      xx88   A  25.6490953  DAYS      OS Overall Survival    1  25.64909528 #> 89      xx89   A 352.4383084  DAYS      OS Overall Survival    1 352.43830838 #> 90      xx90   A  33.8799882  DAYS      OS Overall Survival    1  33.87998822 #> 91      xx91   A  15.8860250  DAYS      OS Overall Survival    1  15.88602505 #> 92      xx92  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Overall Survival    1 453.87552714 #> 105    xx105   A  13.7869996  DAYS      OS Overall Survival    1  13.78699955 #> 106    xx106   A 350.2207221  DAYS      OS Overall Survival    0 350.22072212 #> 107    xx107   A  89.6167692  DAYS      OS Overall Survival    0  89.61676923 #> 108    xx108   A   0.2046028  DAYS      OS Overall Survival    1   0.20460281 #> 109    xx109   A  30.8717178  DAYS      OS Overall Survival    0  30.87171779 #> 110    xx110   A 280.3909651  DAYS      OS Overall Survival    1 280.39096507 #> 111    xx111   A  84.7166775  DAYS      OS Overall Survival    1  84.71667753 #> 112    xx112   A   8.3998372  DAYS      OS Overall Survival    1   8.39983716 #> 113    xx113   A 135.1019149  DAYS      OS Overall Survival    1 135.10191489 #> 114    xx114   A 126.7874323  DAYS      OS Overall Survival    1 126.78743229 #> 115    xx115   A 174.5461325  DAYS      OS Overall Survival    0 174.54613253 #> 116    xx116   A 161.5846889  DAYS      OS Overall Survival    0 161.58468886 #> 117    xx117   A  66.0390115  DAYS      OS Overall Survival    1  66.03901152 #> 118    xx118   A 104.5408470  DAYS      OS Overall Survival    1 104.54084695 #> 119    xx119   A 209.6796639  DAYS      OS Overall Survival    0 209.67966393 #> 120    xx120   A   1.2808132  DAYS      OS Overall Survival    1   1.28081324 #> 121    xx121   A  45.4720260  DAYS      OS Overall Survival    1  45.47202599 #> 122    xx122   A  43.6389358  DAYS      OS Overall Survival    1  43.63893577 #> 123    xx123   A  13.1747627  DAYS      OS Overall Survival    1  13.17476269 #> 124    xx124   A 204.8322250  DAYS      OS Overall Survival    0 204.83222501 #> 125    xx125   A  76.2621398  DAYS      OS Overall Survival    0  76.26213977 #> 126    xx126   A 179.5550296  DAYS      OS Overall Survival    1 179.55502961 #> 127    xx127   A 153.0189691  DAYS      OS Overall Survival    1 153.01896914 #> 128    xx128   A   2.0300958  DAYS      OS Overall Survival    1   2.03009579 #> 129    xx129   A 168.2281259  DAYS      OS Overall Survival    0 168.22812592 #> 130    xx130   A  55.0706664  DAYS      OS Overall Survival    1  55.07066639 #> 131    xx131   A  31.4891944  DAYS      OS Overall Survival    1  31.48919436 #> 132    xx132   A 168.4294215  DAYS      OS Overall Survival    1 168.42942147 #> 133    xx133   A 213.0554394  DAYS      OS Overall Survival    0 213.05543937 #> 134    xx134   A  58.1487885  DAYS      OS Overall Survival    0  58.14878850 #> 135    xx135   A 139.0211403  DAYS      OS Overall Survival    1 139.02114031 #> 136    xx136   A 104.1193509  DAYS      OS Overall Survival    1 104.11935093 #> 137    xx137   A  46.1566080  DAYS      OS Overall Survival    1  46.15660801 #> 138    xx138   A 158.4800477  DAYS      OS Overall Survival    1 158.48004773 #> 139    xx139   A 295.1617767  DAYS      OS Overall Survival    0 295.16177671 #> 140    xx140   A  42.5864914  DAYS      OS Overall Survival    1  42.58649144 #> 141    xx141   A 127.9743928  DAYS      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30.82732713 #> 154    xx154   A  10.2380585  DAYS      OS Overall Survival    0  10.23805854 #> 155    xx155   A 214.0049455  DAYS      OS Overall Survival    1 214.00494552 #> 156    xx156   A  12.6075825  DAYS      OS Overall Survival    1  12.60758248 #> 157    xx157   A 185.7438835  DAYS      OS Overall Survival    1 185.74388353 #> 158    xx158   A 210.5303223  DAYS      OS Overall Survival    1 210.53032231 #> 159    xx159   A  35.8691523  DAYS      OS Overall Survival    0  35.86915231 #> 160    xx160   A  28.2886213  DAYS      OS Overall Survival    1  28.28862126 #> 161    xx161   A  42.1096005  DAYS      OS Overall Survival    0  42.10960050 #> 162    xx162   A 132.0808389  DAYS      OS Overall Survival    1 132.08083893 #> 163    xx163   A 158.7392013  DAYS      OS Overall Survival    1 158.73920134 #> 164    xx164   A 208.5095081  DAYS      OS Overall Survival    0 208.50950815 #> 165    xx165   A 124.8827926  DAYS      OS Overall Survival    0 124.88279256 #> 166    xx166  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Overall Survival    0  11.35273643 #> 179    xx179   A   8.7321192  DAYS      OS Overall Survival    1   8.73211923 #> 180    xx180   A  81.6143745  DAYS      OS Overall Survival    0  81.61437447 #> 181    xx181   A  48.3479226  DAYS      OS Overall Survival    1  48.34792256 #> 182    xx182   A  62.6601429  DAYS      OS Overall Survival    1  62.66014290 #> 183    xx183   A 187.8449785  DAYS      OS Overall Survival    1 187.84497850 #> 184    xx184   A 114.1935322  DAYS      OS Overall Survival    0 114.19353215 #> 185    xx185   A 500.0000000  DAYS      OS Overall Survival    1 500.00000000 #> 186    xx186   A 151.8747627  DAYS      OS Overall Survival    0 151.87476265 #> 187    xx187   A  43.0372554  DAYS      OS Overall Survival    1  43.03725543 #> 188    xx188   A  58.9000652  DAYS      OS Overall Survival    1  58.90006520 #> 189    xx189   A  44.1549084  DAYS      OS Overall Survival    0  44.15490835 #> 190    xx190   A  39.9270162  DAYS      OS Overall Survival    0  39.92701624 #> 191    xx191   A  10.3717562  DAYS      OS Overall Survival    0  10.37175625 #> 192    xx192   A  25.5091635  DAYS      OS Overall Survival    0  25.50916355 #> 193    xx193   A 500.0000000  DAYS      OS Overall Survival    1 500.00000000 #> 194    xx194   A 108.9450103  DAYS      OS Overall Survival    1 108.94501035 #> 195    xx195   A 101.7250316  DAYS      OS Overall Survival    1 101.72503160 #> 196    xx196   A  56.4060157  DAYS      OS Overall Survival    1  56.40601567 #> 197    xx197   A 129.6704036  DAYS      OS Overall Survival    0 129.67040360 #> 198    xx198   A  47.0546355  DAYS      OS Overall Survival    1  47.05463548 #> 199    xx199   A 169.4244480  DAYS      OS Overall Survival    0 169.42444797 #> 200    xx200   A  20.9988640  DAYS      OS Overall Survival    1  20.99886399 #> 201    xx201   A   6.5182421  DAYS      OS Overall Survival    1   6.51824211 #> 202    xx202   A  66.0441764  DAYS      OS Overall Survival    1  66.04417641 #> 203    xx203   A 329.8200403  DAYS      OS Overall Survival    1 329.82004030 #> 204    xx204   A 130.9970045  DAYS      OS Overall Survival    0 130.99700454 #> 205    xx205   A  32.8199139  DAYS      OS Overall Survival    1  32.81991390 #> 206    xx206   A 111.9904588  DAYS      OS Overall Survival    0 111.99045884 #> 207    xx207   A 259.3829166  DAYS      OS Overall Survival    1 259.38291664 #> 208    xx208   A  48.0823337  DAYS      OS Overall Survival    1  48.08233367 #> 209    xx209   A 373.8745493  DAYS      OS Overall Survival    1 373.87454926 #> 210    xx210   A 500.0000000  DAYS      OS Overall Survival    1 500.00000000 #> 211    xx211   A  25.5794699  DAYS      OS Overall Survival    0  25.57946992 #> 212    xx212   A 362.2067038  DAYS      OS Overall Survival    0 362.20670383 #> 213    xx213   A  49.2037680  DAYS      OS Overall Survival    0  49.20376800 #> 214    xx214   A  12.3885075  DAYS      OS Overall Survival    1  12.38850749 #> 215    xx215   A  71.2672138  DAYS      OS Overall Survival    1  71.26721381 #> 216    xx216   A   3.3942755  DAYS      OS Overall Survival    0   3.39427545 #> 217    xx217   A  34.0987472  DAYS      OS Overall Survival    1  34.09874722 #> 218    xx218   A  91.9824456  DAYS      OS Overall Survival    0  91.98244560 #> 219    xx219   A 320.7036093  DAYS      OS Overall Survival    0 320.70360935 #> 220    xx220   A 178.5031223  DAYS      OS Overall Survival    1 178.50312226 #> 221    xx221   A 116.0163469  DAYS      OS Overall Survival    1 116.01634691 #> 222    xx222   A 134.5847123  DAYS      OS Overall Survival    1 134.58471230 #> 223    xx223   A 246.1299544  DAYS      OS Overall Survival    1 246.12995441 #> 224    xx224   A 132.8690671  DAYS      OS Overall Survival    1 132.86906709 #> 225    xx225   A  25.3781343  DAYS      OS Overall Survival    1  25.37813425 #> 226    xx226   A 180.2837518  DAYS      OS Overall Survival    1 180.28375183 #> 227    xx227   A  12.8976681  DAYS      OS Overall Survival    0  12.89766811 #> 228    xx228   A  48.7539111  DAYS      OS Overall Survival    0  48.75391109 #> 229    xx229   A 254.5446271  DAYS      OS Overall Survival    0 254.54462707 #> 230    xx230   A   8.3675931  DAYS      OS Overall Survival    0   8.36759307 #> 231    xx231   A  30.4157094  DAYS      OS Overall Survival    0  30.41570943 #> 232    xx232   A  32.3195134  DAYS      OS Overall Survival    1  32.31951339 #> 233    xx233   A   5.8283381  DAYS      OS Overall Survival    0   5.82833812 #> 234    xx234   A 409.5208072  DAYS      OS Overall Survival    1 409.52080716 #> 235    xx235   A  80.7172811  DAYS      OS Overall Survival    1  80.71728110 #> 236    xx236   A  29.5736758  DAYS      OS Overall Survival    1  29.57367581 #> 237    xx237   A  88.3243550  DAYS      OS Overall Survival    0  88.32435496 #> 238    xx238   A  16.6777043  DAYS      OS Overall Survival    0  16.67770427 #> 239    xx239   A 201.8084701  DAYS      OS Overall Survival    0 201.80847006 #> 240    xx240  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Overall Survival    1 204.71319623 #> 253    xx253   A 221.0842591  DAYS      OS Overall Survival    0 221.08425909 #> 254    xx254   A 126.8931381  DAYS      OS Overall Survival    1 126.89313809 #> 255    xx255   A   6.8416124  DAYS      OS Overall Survival    0   6.84161237 #> 256    xx256   A  39.5099966  DAYS      OS Overall Survival    1  39.50999657 #> 257    xx257   A 303.3503769  DAYS      OS Overall Survival    1 303.35037691 #> 258    xx258   A 103.3599337  DAYS      OS Overall Survival    1 103.35993372 #> 259    xx259   A  73.0774123  DAYS      OS Overall Survival    1  73.07741226 #> 260    xx260   A  15.2886098  DAYS      OS Overall Survival    1  15.28860979 #> 261    xx261   A 144.5084460  DAYS      OS Overall Survival    0 144.50844597 #> 262    xx262   A 227.9757161  DAYS      OS Overall Survival    1 227.97571613 #> 263    xx263   A 233.3291179  DAYS      OS Overall Survival    1 233.32911792 #> 264    xx264   A  10.9352310  DAYS      OS Overall Survival    1  10.93523101 #> 265    xx265   A 284.9961410  DAYS      OS Overall Survival    1 284.99614097 #> 266    xx266   A 119.6814391  DAYS      OS Overall Survival    1 119.68143912 #> 267    xx267   A 153.7818843  DAYS      OS Overall Survival    1 153.78188428 #> 268    xx268   A  41.5788383  DAYS      OS Overall Survival    1  41.57883829 #> 269    xx269   A  17.1922456  DAYS      OS Overall Survival    1  17.19224560 #> 270    xx270   A  12.1850070  DAYS      OS Overall Survival    1  12.18500697 #> 271    xx271   A 301.7347889  DAYS      OS Overall Survival    1 301.73478886 #> 272    xx272   A  93.7104093  DAYS      OS Overall Survival    1  93.71040927 #> 273    xx273   A 414.9602508  DAYS      OS Overall Survival    0 414.96025076 #> 274    xx274   A 203.2206009  DAYS      OS Overall Survival    1 203.22060092 #> 275    xx275   A  81.0661957  DAYS      OS Overall Survival    1  81.06619570 #> 276    xx276   A 135.8094446  DAYS      OS Overall Survival    0 135.80944463 #> 277    xx277   A 113.1377720  DAYS      OS Overall Survival    1 113.13777204 #> 278    xx278   A 406.0771660  DAYS      OS Overall Survival    1 406.07716604 #> 279    xx279   A 136.1992022  DAYS      OS Overall Survival    0 136.19920218 #> 280    xx280   A 110.6752035  DAYS      OS Overall Survival    1 110.67520350 #> 281    xx281   A 282.6318510  DAYS      OS Overall Survival    1 282.63185100 #> 282    xx282   A 245.5501089  DAYS      OS Overall Survival    1 245.55010890 #> 283    xx283   A 101.3714715  DAYS      OS Overall Survival    1 101.37147148 #> 284    xx284   A  14.6109023  DAYS      OS Overall Survival    0  14.61090228 #> 285    xx285   A   3.2894058  DAYS      OS Overall Survival    1   3.28940576 #> 286    xx286   A  20.4779808  DAYS      OS Overall Survival    1  20.47798081 #> 287    xx287   A   0.3321626  DAYS      OS Overall Survival    0   0.33216257 #> 288    xx288   A 178.9681939  DAYS      OS Overall Survival    1 178.96819391 #> 289    xx289   A  27.2088851  DAYS      OS 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204.60858275 #> 302    xx302   A  34.5820833  DAYS      OS Overall Survival    1  34.58208330 #> 303    xx303   A  37.8814246  DAYS      OS Overall Survival    1  37.88142464 #> 304    xx304   A  88.6145506  DAYS      OS Overall Survival    1  88.61455059 #> 305    xx305   A  98.5382896  DAYS      OS Overall Survival    0  98.53828958 #> 306    xx306   A 237.8774307  DAYS      OS Overall Survival    0 237.87743067 #> 307    xx307   A  76.6524844  DAYS      OS Overall Survival    0  76.65248445 #> 308    xx308   A  91.8647559  DAYS      OS Overall Survival    1  91.86475594 #> 309    xx309   A  51.6937890  DAYS      OS Overall Survival    1  51.69378901 #> 310    xx310   A  29.5678669  DAYS      OS Overall Survival    1  29.56786685 #> 311    xx311   A  71.2587985  DAYS      OS Overall Survival    0  71.25879852 #> 312    xx312   A 145.2570428  DAYS      OS Overall Survival    0 145.25704283 #> 313    xx313   A 249.9233069  DAYS      OS Overall Survival    1 249.92330689 #> 314    xx314 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OS Overall Survival    1  93.24085921 #> 327    xx327   A 101.6701105  DAYS      OS Overall Survival    1 101.67011046 #> 328    xx328   A 247.7760192  DAYS      OS Overall Survival    1 247.77601921 #> 329    xx329   A  43.3221132  DAYS      OS Overall Survival    1  43.32211317 #> 330    xx330   A 130.9639163  DAYS      OS Overall Survival    1 130.96391627 #> 331    xx331   A  89.7755695  DAYS      OS Overall Survival    0  89.77556948 #> 332    xx332   A  37.7626359  DAYS      OS Overall Survival    0  37.76263590 #> 333    xx333   A 236.1039880  DAYS      OS Overall Survival    1 236.10398798 #> 334    xx334   A   5.5735189  DAYS      OS Overall Survival    1   5.57351887 #> 335    xx335   A  15.9789179  DAYS      OS Overall Survival    1  15.97891788 #> 336    xx336   A 219.5385758  DAYS      OS Overall Survival    1 219.53857583 #> 337    xx337   A 122.1766793  DAYS      OS Overall Survival    1 122.17667929 #> 338    xx338   A  85.2629108  DAYS      OS Overall Survival    0  85.26291085 #> 339    xx339   A  72.1670941  DAYS      OS Overall Survival    0  72.16709408 #> 340    xx340   A  49.3435304  DAYS      OS Overall Survival    0  49.34353036 #> 341    xx341   A  84.3199651  DAYS      OS Overall Survival    1  84.31996511 #> 342    xx342   A 102.0646836  DAYS      OS Overall Survival    1 102.06468356 #> 343    xx343   A  99.9203054  DAYS      OS Overall Survival    1  99.92030535 #> 344    xx344   A 105.9591180  DAYS      OS Overall Survival    1 105.95911796 #> 345    xx345   A 341.3657643  DAYS      OS Overall Survival    1 341.36576428 #> 346    xx346   A  61.1170188  DAYS      OS Overall Survival    0  61.11701880 #> 347    xx347   A 273.1257002  DAYS      OS Overall Survival    1 273.12570021 #> 348    xx348   A  83.7537322  DAYS      OS Overall Survival    1  83.75373223 #> 349    xx349   A 287.8308610  DAYS      OS Overall Survival    1 287.83086098 #> 350    xx350   A  85.1705400  DAYS      OS Overall Survival    1  85.17054003 #> 351    xx351  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OS Overall Survival    0  27.75993926 #> 882  yyxx382   C 141.6016337  DAYS      OS Overall Survival    1  18.68147096 #> 883  yyxx383   C 500.0000000  DAYS      OS Overall Survival    1   0.14520024 #> 884  yyxx384   C  67.3416893  DAYS      OS Overall Survival    0   8.37358088 #> 885  yyxx385   C 293.6815759  DAYS      OS Overall Survival    0  19.60177973 #> 886  yyxx386   C 170.5776237  DAYS      OS Overall Survival    0   9.10448872 #> 887  yyxx387   C  74.4971040  DAYS      OS Overall Survival    1  95.12412463 #> 888  yyxx388   C 252.7425757  DAYS      OS Overall Survival    1  17.35079729 #> 889  yyxx389   C 370.3157123  DAYS      OS Overall Survival    0 437.57881136 #> 890  yyxx390   C  18.4767328  DAYS      OS Overall Survival    1 286.40736664 #> 891  yyxx391   C 242.3122311  DAYS      OS Overall Survival    1  96.52726878 #> 892  yyxx392   C 108.6286686  DAYS      OS Overall Survival    1 113.16031692 #> 893  yyxx393   C 135.1588000  DAYS      OS Overall Survival    0 110.35494805 #> 894  yyxx394   C  13.4000154  DAYS      OS Overall Survival    0  85.24104005 #> 895  yyxx395   C  50.6666237  DAYS      OS Overall Survival    0  14.56825937 #> 896  yyxx396   C 181.3283312  DAYS      OS Overall Survival    1 243.78727731 #> 897  yyxx397   C  13.0073322  DAYS      OS Overall Survival    1  65.02737517 #> 898  yyxx398   C 357.7553493  DAYS      OS Overall Survival    1  59.72295171 #> 899  yyxx399   C 180.9009377  DAYS      OS Overall Survival    0   1.18789729 #> 900  yyxx400   C 242.1178978  DAYS      OS Overall Survival    1  35.39540167 #> 901  yyxx401   C  60.2893293  DAYS      OS Overall Survival    1  94.84422956 #> 902  yyxx402   C  93.4905863  DAYS      OS Overall Survival    1  41.46343687 #> 903  yyxx403   C  52.7997845  DAYS      OS Overall Survival    0 119.98769098 #> 904  yyxx404   C 350.2654152  DAYS      OS Overall Survival    1 128.13968895 #> 905  yyxx405   C 241.1774392  DAYS      OS Overall Survival    1  66.82508584 #> 906  yyxx406 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OS Overall Survival    1 127.75039430 #> 919  yyxx419   C  41.6267738  DAYS      OS Overall Survival    0  10.89958404 #> 920  yyxx420   C  15.8302898  DAYS      OS Overall Survival    0  40.86016804 #> 921  yyxx421   C  86.8743175  DAYS      OS Overall Survival    0  87.81394704 #> 922  yyxx422   C 350.3644114  DAYS      OS Overall Survival    1 223.19097765 #> 923  yyxx423   C 241.5604544  DAYS      OS Overall Survival    0  32.95351409 #> 924  yyxx424   C 318.7833017  DAYS      OS Overall Survival    0   8.11811192 #> 925  yyxx425   C 359.1658176  DAYS      OS Overall Survival    1 258.22347628 #> 926  yyxx426   C  14.1516358  DAYS      OS Overall Survival    0  18.04976559 #> 927  yyxx427   C 104.8671198  DAYS      OS Overall Survival    1   3.65964496 #> 928  yyxx428   C 158.0674152  DAYS      OS Overall Survival    1  42.66199303 #> 929  yyxx429   C  53.4114635  DAYS      OS Overall Survival    0  32.33421493 #> 930  yyxx430   C  52.6613128  DAYS      OS Overall Survival    0  25.75751268 #> 931  yyxx431   C 402.2578995  DAYS      OS Overall Survival    1  88.21501732 #> 932  yyxx432   C   2.4121713  DAYS      OS Overall Survival    1 131.90296010 #> 933  yyxx433   C 189.4981770  DAYS      OS Overall Survival    0  44.52073874 #> 934  yyxx434   C   4.2866692  DAYS      OS Overall Survival    1 254.86526980 #> 935  yyxx435   C 256.4404196  DAYS      OS Overall Survival    1  25.41412967 #> 936  yyxx436   C 452.7208595  DAYS      OS Overall Survival    0  86.98203003 #> 937  yyxx437   C  31.7437313  DAYS      OS Overall Survival    1  37.00076281 #> 938  yyxx438   C 370.0496553  DAYS      OS Overall Survival    1 263.48863428 #> 939  yyxx439   C 299.0591141  DAYS      OS Overall Survival    1 111.49580995 #> 940  yyxx440   C 134.9211996  DAYS      OS Overall Survival    1  70.26925420 #> 941  yyxx441   C  34.0150132  DAYS      OS Overall Survival    0 147.78736954 #> 942  yyxx442   C  36.3408479  DAYS      OS Overall Survival    0 196.43090995 #> 943  yyxx443  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Overall Survival    1 117.49511217 #> 956  yyxx456   C  20.0770245  DAYS      OS Overall Survival    0  93.11604033 #> 957  yyxx457   C  36.0369583  DAYS      OS Overall Survival    0  22.86049975 #> 958  yyxx458   C 184.0466008  DAYS      OS Overall Survival    0  73.90224221 #> 959  yyxx459   C  76.6013852  DAYS      OS Overall Survival    0  66.90302316 #> 960  yyxx460   C 207.4070734  DAYS      OS Overall Survival    1   3.32411004 #> 961  yyxx461   C 154.3107802  DAYS      OS Overall Survival    1  36.13140895 #> 962  yyxx462   C 106.6420898  DAYS      OS Overall Survival    0 208.62293514 #> 963  yyxx463   C 205.0363326  DAYS      OS Overall Survival    0  38.36725822 #> 964  yyxx464   C  20.9126041  DAYS      OS Overall Survival    0  56.29689086 #> 965  yyxx465   C  18.6336849  DAYS      OS Overall Survival    1  15.44590687 #> 966  yyxx466   C 136.3800132  DAYS      OS Overall Survival    0  17.28062264 #> 967  yyxx467   C 316.3884586  DAYS      OS Overall Survival    1  61.55896497 #> 968  yyxx468   C 141.3017821  DAYS      OS Overall Survival    1  38.53035418 #> 969  yyxx469   C  13.2112969  DAYS      OS Overall Survival    1  78.26857427 #> 970  yyxx470   C  99.6448581  DAYS      OS Overall Survival    1  66.54501610 #> 971  yyxx471   C 135.2069709  DAYS      OS Overall Survival    0  11.75128101 #> 972  yyxx472   C  61.6891086  DAYS      OS Overall Survival    1  27.14682548 #> 973  yyxx473   C 101.1286638  DAYS      OS Overall Survival    1   0.04489938 #> 974  yyxx474   C 166.2426128  DAYS      OS Overall Survival    0 306.36501330 #> 975  yyxx475   C  79.8316941  DAYS      OS Overall Survival    1  39.29798097 #> 976  yyxx476   C 234.7546470  DAYS      OS Overall Survival    1 385.93370361 #> 977  yyxx477   C  94.7052270  DAYS      OS Overall Survival    1  55.42401529 #> 978  yyxx478   C 168.1161018  DAYS      OS Overall Survival    0  48.41662491 #> 979  yyxx479   C 146.5126322  DAYS      OS Overall Survival    1 107.22939825 #> 980  yyxx480  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Overall Survival    0 122.64731604 #> 993  yyxx493   C  87.0820950  DAYS      OS Overall Survival    0 229.33116122 #> 994  yyxx494   C 190.6912802  DAYS      OS Overall Survival    0  29.07607607 #> 995  yyxx495   C  49.8173522  DAYS      OS Overall Survival    1  86.44606009 #> 996  yyxx496   C 215.4027340  DAYS      OS Overall Survival    0  32.22703111 #> 997  yyxx497   C 111.1416159  DAYS      OS Overall Survival    0  73.37981988 #> 998  yyxx498   C  17.0752816  DAYS      OS Overall Survival    0   6.13227059 #> 999  yyxx499   C  98.2021703  DAYS      OS Overall Survival    0  73.69939882 #> 1000 yyxx500   C  97.5095606  DAYS      OS Overall Survival    0  71.52985288 #>      EVENT #> 1        0 #> 2        0 #> 3        0 #> 4        0 #> 5        0 #> 6        1 #> 7        0 #> 8        1 #> 9        0 #> 10       0 #> 11       0 #> 12       0 #> 13       1 #> 14       1 #> 15       0 #> 16       0 #> 17       0 #> 18       1 #> 19       0 #> 20       0 #> 21       1 #> 22     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237      1 #> 238      1 #> 239      1 #> 240      0 #> 241      0 #> 242      1 #> 243      0 #> 244      0 #> 245      0 #> 246      0 #> 247      0 #> 248      1 #> 249      0 #> 250      1 #> 251      0 #> 252      0 #> 253      1 #> 254      0 #> 255      1 #> 256      0 #> 257      0 #> 258      0 #> 259      0 #> 260      0 #> 261      1 #> 262      0 #> 263      0 #> 264      0 #> 265      0 #> 266      0 #> 267      0 #> 268      0 #> 269      0 #> 270      0 #> 271      0 #> 272      0 #> 273      1 #> 274      0 #> 275      0 #> 276      1 #> 277      0 #> 278      0 #> 279      1 #> 280      0 #> 281      0 #> 282      0 #> 283      0 #> 284      1 #> 285      0 #> 286      0 #> 287      1 #> 288      0 #> 289      0 #> 290      0 #> 291      1 #> 292      0 #> 293      0 #> 294      0 #> 295      1 #> 296      1 #> 297      1 #> 298      1 #> 299      0 #> 300      0 #> 301      0 #> 302      0 #> 303      0 #> 304      0 #> 305      1 #> 306      1 #> 307      1 #> 308    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523      1 #> 524      1 #> 525      1 #> 526      1 #> 527      1 #> 528      1 #> 529      1 #> 530      1 #> 531      1 #> 532      1 #> 533      1 #> 534      1 #> 535      1 #> 536      1 #> 537      1 #> 538      1 #> 539      0 #> 540      1 #> 541      1 #> 542      1 #> 543      1 #> 544      1 #> 545      1 #> 546      1 #> 547      1 #> 548      1 #> 549      1 #> 550      1 #> 551      1 #> 552      1 #> 553      1 #> 554      1 #> 555      1 #> 556      1 #> 557      1 #> 558      1 #> 559      1 #> 560      1 #> 561      1 #> 562      1 #> 563      1 #> 564      1 #> 565      1 #> 566      1 #> 567      1 #> 568      1 #> 569      1 #> 570      1 #> 571      1 #> 572      1 #> 573      1 #> 574      1 #> 575      1 #> 576      1 #> 577      1 #> 578      1 #> 579      1 #> 580      1 #> 581      1 #> 582      1 #> 583      1 #> 584      1 #> 585      1 #> 586      1 #> 587      1 #> 588      1 #> 589      1 #> 590      1 #> 591      1 #> 592      1 #> 593      1 #> 594    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666      1 #> 667      1 #> 668      1 #> 669      1 #> 670      1 #> 671      1 #> 672      1 #> 673      1 #> 674      1 #> 675      1 #> 676      1 #> 677      1 #> 678      1 #> 679      1 #> 680      1 #> 681      1 #> 682      1 #> 683      1 #> 684      1 #> 685      1 #> 686      1 #> 687      1 #> 688      1 #> 689      1 #> 690      1 #> 691      1 #> 692      0 #> 693      1 #> 694      1 #> 695      1 #> 696      1 #> 697      1 #> 698      1 #> 699      1 #> 700      1 #> 701      1 #> 702      1 #> 703      1 #> 704      1 #> 705      1 #> 706      1 #> 707      1 #> 708      1 #> 709      1 #> 710      1 #> 711      1 #> 712      1 #> 713      1 #> 714      1 #> 715      1 #> 716      1 #> 717      1 #> 718      1 #> 719      1 #> 720      1 #> 721      1 #> 722      1 #> 723      1 #> 724      1 #> 725      1 #> 726      1 #> 727      1 #> 728      1 #> 729      1 #> 730      1 #> 731      1 #> 732      1 #> 733      1 #> 734      1 #> 735      1 #> 736      1 #> 737      1 #> 738      1 #> 739      1 #> 740      1 #> 741      1 #> 742      1 #> 743      1 #> 744      1 #> 745      1 #> 746      1 #> 747      1 #> 748      1 #> 749      1 #> 750      1 #> 751      1 #> 752      1 #> 753      1 #> 754      1 #> 755      1 #> 756      1 #> 757      1 #> 758      1 #> 759      1 #> 760      1 #> 761      1 #> 762      1 #> 763      1 #> 764      1 #> 765      1 #> 766      1 #> 767      1 #> 768      1 #> 769      1 #> 770      1 #> 771      1 #> 772      1 #> 773      1 #> 774      1 #> 775      1 #> 776      1 #> 777      1 #> 778      1 #> 779      1 #> 780      1 #> 781      1 #> 782      1 #> 783      1 #> 784      1 #> 785      1 #> 786      1 #> 787      1 #> 788      1 #> 789      1 #> 790      1 #> 791      1 #> 792      1 #> 793      1 #> 794      1 #> 795      1 #> 796      1 #> 797      1 #> 798      1 #> 799      1 #> 800      1 #> 801      1 #> 802      1 #> 803      1 #> 804      1 #> 805      1 #> 806      1 #> 807      1 #> 808      0 #> 809      1 #> 810      1 #> 811      1 #> 812      1 #> 813      1 #> 814      1 #> 815      1 #> 816      1 #> 817      1 #> 818      1 #> 819      1 #> 820      1 #> 821      1 #> 822      1 #> 823      1 #> 824      1 #> 825      1 #> 826      1 #> 827      1 #> 828      1 #> 829      1 #> 830      1 #> 831      1 #> 832      1 #> 833      1 #> 834      1 #> 835      1 #> 836      1 #> 837      1 #> 838      1 #> 839      1 #> 840      1 #> 841      1 #> 842      1 #> 843      1 #> 844      1 #> 845      1 #> 846      1 #> 847      1 #> 848      1 #> 849      1 #> 850      1 #> 851      1 #> 852      1 #> 853      1 #> 854      1 #> 855      0 #> 856      1 #> 857      1 #> 858      1 #> 859      1 #> 860      1 #> 861      1 #> 862      1 #> 863      1 #> 864      1 #> 865      1 #> 866      1 #> 867      1 #> 868      1 #> 869      1 #> 870      1 #> 871      1 #> 872      1 #> 873      1 #> 874      1 #> 875      1 #> 876      1 #> 877      1 #> 878      1 #> 879      1 #> 880      1 #> 881      1 #> 882      1 #> 883      1 #> 884      1 #> 885      1 #> 886      1 #> 887      1 #> 888      1 #> 889      1 #> 890      1 #> 891      1 #> 892      1 #> 893      1 #> 894      1 #> 895      1 #> 896      1 #> 897      1 #> 898      1 #> 899      1 #> 900      1 #> 901      1 #> 902      1 #> 903      1 #> 904      1 #> 905      1 #> 906      1 #> 907      1 #> 908      1 #> 909      1 #> 910      1 #> 911      1 #> 912      1 #> 913      1 #> 914      1 #> 915      1 #> 916      1 #> 917      1 #> 918      1 #> 919      1 #> 920      1 #> 921      1 #> 922      1 #> 923      1 #> 924      1 #> 925      1 #> 926      1 #> 927      1 #> 928      1 #> 929      1 #> 930      1 #> 931      1 #> 932      1 #> 933      1 #> 934      1 #> 935      1 #> 936      1 #> 937      1 #> 938      1 #> 939      1 #> 940      1 #> 941      1 #> 942      1 #> 943      1 #> 944      1 #> 945      1 #> 946      1 #> 947      1 #> 948      1 #> 949      1 #> 950      1 #> 951      1 #> 952      1 #> 953      0 #> 954      1 #> 955      1 #> 956      1 #> 957      1 #> 958      1 #> 959      1 #> 960      1 #> 961      1 #> 962      1 #> 963      1 #> 964      1 #> 965      1 #> 966      1 #> 967      1 #> 968      1 #> 969      1 #> 970      1 #> 971      1 #> 972      1 #> 973      1 #> 974      1 #> 975      1 #> 976      1 #> 977      1 #> 978      1 #> 979      1 #> 980      1 #> 981      1 #> 982      1 #> 983      1 #> 984      1 #> 985      1 #> 986      1 #> 987      1 #> 988      1 #> 989      1 #> 990      1 #> 991      1 #> 992      1 #> 993      1 #> 994      1 #> 995      1 #> 996      1 #> 997      1 #> 998      1 #> 999      1 #> 1000     1  ### AgD # Baseline aggregate data for the comparator population agd #>        STUDY          ARM   N AGE_MEAN AGE_MEDIAN AGE_SD SEX_MALE_COUNT #> 1 Study_XXXX        Total 300       51         49   3.25            147 #> 2 Study_XXXX Intervention  NA       NA         NA     NA             NA #> 3 Study_XXXX   Comparator  NA       NA         NA     NA             NA #>   ECOG0_COUNT SMOKE_COUNT N_PR_THER_MEDIAN #> 1         105          58                2 #> 2          NA          NA               NA #> 3          NA          NA               NA  # for time-to-event endpoints, pseudo IPD from digitalized KM pseudo_ipd_twt #>            Time Event ARM #> 1    20.2311676     1   B #> 2    28.7679537     1   B #> 3    41.0662129     0   B #> 4     0.8492261     1   B #> 5     9.0521882     0   B #> 6     3.4450075     1   B #> 7    88.7049039     0   B #> 8    75.5205919     0   B #> 9   102.0480708     0   B #> 10   61.1832674     0   B #> 11   44.6248777     1   B #> 12  417.7060892     0   B #> 13  188.0853029     1   B #> 14  135.4086771     1   B #> 15   14.4693075     1   B #> 16   19.2886739     1   B #> 17  253.9413828     0   B #> 18   18.8265045     1   B #> 19   42.5311133     0   B #> 20  203.2845550     0   B #> 21   32.2845542     1   B #> 22    3.0685160     1   B #> 23   53.3559798     1   B #> 24   69.5240787     0   B #> 25   38.8236166     1   B #> 26  204.6022754     0   B #> 27  184.5103590     0   B #> 28   83.5830935     0   B #> 29  114.7067767     1   B #> 30   31.4008382     1   B #> 31   65.7530859     0   B #> 32  163.6438166     1   B #> 33  103.4746398     0   B #> 34  115.6529216     0   B #> 35  119.8663712     1   B #> 36   39.7824824     0   B #> 37   26.2260987     1   B #> 38  168.2133266     1   B #> 39   77.4276997     0   B #> 40  113.1300571     0   B #> 41  341.8226476     0   B #> 42    4.6203917     1   B #> 43   76.1546928     1   B #> 44   31.3239259     1   B #> 45  139.8137748     1   B #> 46   26.2748075     1   B #> 47   78.8443008     0   B #> 48  150.1996343     0   B #> 49    7.5020884     1   B #> 50   27.0121949     1   B #> 51  157.8603589     1   B #> 52  196.4246807     0   B #> 53   21.6935437     0   B #> 54  140.2541753     1   B #> 55   12.1075238     0   B #> 56  107.3694875     1   B #> 57   37.8779261     1   B #> 58  121.0960158     0   B #> 59   20.3595135     1   B #> 60   41.0376828     0   B #> 61   27.4099098     1   B #> 62   14.2204744     1   B #> 63   49.0816396     1   B #> 64   91.8865159     1   B #> 65   20.8240812     0   B #> 66   19.7480647     1   B #> 67   13.4869944     0   B #> 68  297.0789867     1   B #> 69   35.3155408     1   B #> 70   61.3301293     1   B #> 71  236.3826316     0   B #> 72   37.6872463     1   B #> 73   12.4915818     0   B #> 74  117.1516860     1   B #> 75   74.0347874     1   B #> 76   31.8360820     1   B #> 77    6.1533491     0   B #> 78   58.6084245     1   B #> 79    3.7608198     0   B #> 80   32.1776180     1   B #> 81  123.7513692     1   B #> 82  191.6321564     1   B #> 83   49.7949898     0   B #> 84   95.9720009     0   B #> 85   35.3873079     0   B #> 86   93.7520019     1   B #> 87    8.2221920     1   B #> 88   69.4935935     1   B #> 89  115.3801046     0   B #> 90  201.1106058     0   B #> 91  101.8512269     1   B #> 92   16.3430735     0   B #> 93   73.2620925     1   B #> 94   20.9476736     0   B #> 95   13.2074189     1   B #> 96  133.1594500     0   B #> 97    6.7140516     1   B #> 98    2.0758651     0   B #> 99  155.7676131     1   B #> 100 324.6217046     0   B #> 101  57.1030595     1   B #> 102   3.6143976     1   B #> 103  35.3912534     0   B #> 104  48.2754867     1   B #> 105  24.2416986     0   B #> 106   3.2412819     1   B #> 107  77.1943697     0   B #> 108 401.8506175     1   B #> 109  52.2920989     0   B #> 110  14.2519825     0   B #> 111 195.5002523     1   B #> 112   3.1712902     1   B #> 113  58.4270646     1   B #> 114  18.5735263     1   B #> 115  59.6546380     1   B #> 116   6.4806603     1   B #> 117  75.1656036     1   B #> 118   1.7198339     1   B #> 119  84.2445319     0   B #> 120  18.3219669     0   B #> 121  27.3660973     0   B #> 122  23.8855711     0   B #> 123  68.8229804     1   B #> 124  18.9849356     1   B #> 125  37.3202892     1   B #> 126  48.0896160     1   B #> 127  78.4347848     1   B #> 128 128.6077718     0   B #> 129  91.3637782     1   B #> 130  56.9334980     1   B #> 131  13.9279249     1   B #> 132   3.0113133     1   B #> 133  53.6840931     0   B #> 134 109.4392228     1   B #> 135  27.8727602     0   B #> 136  31.3737925     1   B #> 137  36.6028967     0   B #> 138  34.0285658     1   B #> 139  49.9062966     1   B #> 140  47.1405277     0   B #> 141 101.0785894     1   B #> 142  93.6487147     1   B #> 143  20.8436242     1   B #> 144   3.4515079     1   B #> 145  29.8875161     1   B #> 146  63.3996256     0   B #> 147 230.9729773     1   B #> 148 364.3259341     1   B #> 149  12.2323488     1   B #> 150 159.8315468     0   B #> 151 258.5498424     1   B #> 152  24.9889490     0   B #> 153  63.0700888     1   B #> 154 320.4212314     1   B #> 155  90.6121802     1   B #> 156 160.7520774     0   B #> 157  28.8516511     0   B #> 158   8.7919587     0   B #> 159  27.1385849     1   B #> 160  38.2870764     1   B #> 161   7.4210626     0   B #> 162  13.6943879     1   B #> 163 139.8042160     1   B #> 164 170.6566002     0   B #> 165 114.2058151     1   B #> 166 172.4208727     0   B #> 167  39.0678457     1   B #> 168  16.0775600     0   B #> 169  62.4040822     1   B #> 170  95.9224128     1   B #> 171  31.0753223     1   B #> 172   7.3859528     1   B #> 173  13.5637730     0   B #> 174  25.3284722     0   B #> 175 215.0981459     1   B #> 176  13.9126408     1   B #> 177 220.6392416     1   B #> 178  82.5469181     0   B #> 179  10.3249249     0   B #> 180 114.4152727     0   B #> 181  67.4200893     1   B #> 182  16.5377021     1   B #> 183  21.4476172     0   B #> 184 163.3827108     1   B #> 185 114.8396735     0   B #> 186  33.7408843     1   B #> 187  72.9266499     1   B #> 188  85.2637815     1   B #> 189  12.7049720     0   B #> 190  33.5083467     1   B #> 191  64.9965467     1   B #> 192  62.4816013     1   B #> 193  32.6268562     1   B #> 194 230.5868701     1   B #> 195  40.2919970     0   B #> 196  38.4803842     1   B #> 197 116.5770227     1   B #> 198  11.9051673     1   B #> 199  34.7955872     1   B #> 200  48.1972482     1   B #> 201  84.6593786     0   B #> 202 210.0548161     0   B #> 203  32.3201275     0   B #> 204  47.8891543     1   B #> 205  94.4459064     1   B #> 206  27.1427844     1   B #> 207  75.4571413     0   B #> 208  13.0554772     1   B #> 209   1.4030134     1   B #> 210 150.6582394     1   B #> 211  80.4921978     0   B #> 212 102.1683344     1   B #> 213  44.9419892     0   B #> 214 232.5020784     0   B #> 215   2.7979480     0   B #> 216  64.5608166     0   B #> 217  67.5251343     1   B #> 218 129.1025209     1   B #> 219 500.0000000     0   B #> 220  19.5837178     0   B #> 221  70.1227875     0   B #> 222  22.5998941     1   B #> 223  83.5853505     1   B #> 224   8.6680839     1   B #> 225 129.7187580     1   B #> 226  19.3144386     0   B #> 227 333.8879947     1   B #> 228  51.2082623     1   B #> 229  46.6101451     0   B #> 230  73.6926698     1   B #> 231  34.3307012     0   B #> 232  76.4192775     1   B #> 233   2.9805455     0   B #> 234 127.4336784     0   B #> 235   3.4211613     0   B #> 236  54.9236065     1   B #> 237 294.1386975     1   B #> 238  15.2915139     0   B #> 239  56.8481500     1   B #> 240  55.3458512     0   B #> 241   7.7622930     1   B #> 242  93.8619589     0   B #> 243 177.5091814     0   B #> 244  70.2628625     0   B #> 245  41.2949253     1   B #> 246 129.7250459     1   B #> 247   5.5211413     0   B #> 248  38.4697339     1   B #> 249  38.4278985     0   B #> 250  37.9046463     1   B #> 251  29.7580207     0   B #> 252  37.3557166     0   B #> 253   2.3393342     1   B #> 254 116.2388631     0   B #> 255  37.1140694     1   B #> 256  93.2119835     1   B #> 257  16.7345687     0   B #> 258  28.7863941     0   B #> 259  17.0540489     1   B #> 260  60.2527239     0   B #> 261  56.7171853     0   B #> 262  22.2428646     1   B #> 263  75.5904943     0   B #> 264  32.8579123     1   B #> 265 105.5323827     0   B #> 266 124.2150196     1   B #> 267  90.5420118     1   B #> 268  55.0154610     1   B #> 269 215.5597419     1   B #> 270   5.5773040     1   B #> 271 144.2521772     0   B #> 272  69.0486551     1   B #> 273 212.7464896     1   B #> 274 128.9828791     0   B #> 275  26.6127303     1   B #> 276  12.2191633     1   B #> 277  18.6707015     0   B #> 278 175.4929594     0   B #> 279  40.8238759     1   B #> 280  69.7451992     1   B #> 281  47.8510476     0   B #> 282  18.9679808     0   B #> 283  61.2068279     1   B #> 284  44.8460398     1   B #> 285 199.8426419     1   B #> 286  58.7730149     1   B #> 287  64.6149696     1   B #> 288  38.9656553     0   B #> 289  17.7346078     1   B #> 290  64.0457781     1   B #> 291  40.1446851     1   B #> 292  73.5593273     1   B #> 293  68.5812580     0   B #> 294  96.2560394     1   B #> 295  10.5896188     1   B #> 296 111.0420553     0   B #> 297  22.4233793     1   B #> 298   9.0396962     1   B #> 299 143.5751793     0   B #> 300  15.1259399     0   B #> 301  31.9007805     1   C #> 302 224.9210305     1   C #> 303  40.8121892     1   C #> 304   2.8986329     1   C #> 305 122.7150417     1   C #> 306 117.2979372     1   C #> 307 139.8538186     1   C #> 308 198.6359612     1   C #> 309  45.7984108     1   C #> 310  51.5045070     1   C #> 311 284.6505775     1   C #> 312   8.2054956     1   C #> 313  37.3399724     1   C #> 314  78.4380157     1   C #> 315  67.6653058     1   C #> 316  35.8826731     1   C #> 317 206.4266946     1   C #> 318   4.4685532     1   C #> 319  21.7769869     1   C #> 320   9.2258614     1   C #> 321  49.3131530     1   C #> 322  53.7385301     1   C #> 323  80.6121057     1   C #> 324 126.1661283     1   C #> 325   7.9635105     1   C #> 326  16.2166981     1   C #> 327 187.8325651     1   C #> 328  16.7471612     1   C #> 329   8.6766179     1   C #> 330   6.1161740     1   C #> 331 276.2456938     1   C #> 332 361.8332113     1   C #> 333  58.4359040     1   C #> 334  85.4101501     1   C #> 335   9.5951805     1   C #> 336   2.8626645     1   C #> 337   3.8211774     1   C #> 338  14.2525023     1   C #> 339  20.8885622     1   C #> 340 244.8500372     1   C #> 341  94.6601919     1   C #> 342  42.6863882     1   C #> 343  53.2908582     1   C #> 344  34.8085622     1   C #> 345  52.9124016     1   C #> 346 169.4117331     1   C #> 347 107.1202631     1   C #> 348 211.5530765     1   C #> 349  47.4429379     1   C #> 350  43.8325267     1   C #> 351  40.1064231     1   C #> 352  32.2150048     1   C #> 353  56.6569594     1   C #> 354  22.1794129     1   C #> 355  76.2141403     1   C #> 356  83.9255240     1   C #> 357  18.2860990     1   C #> 358  39.1744099     1   C #> 359   6.2007373     1   C #> 360 266.3275331     1   C #> 361  63.3110955     1   C #> 362  17.6230889     1   C #> 363 115.5534530     1   C #> 364  82.5114275     1   C #> 365 186.3398043     1   C #> 366  30.1885913     1   C #> 367  30.0502707     1   C #> 368  48.9121396     1   C #> 369 141.4487129     1   C #> 370 171.7598873     1   C #> 371  20.7614482     1   C #> 372  76.8425139     1   C #> 373  43.2498692     1   C #> 374  68.4211613     1   C #> 375 127.3104848     1   C #> 376  32.4396500     1   C #> 377  71.0596804     1   C #> 378 139.1435945     1   C #> 379 211.5398906     1   C #> 380 376.1052610     1   C #> 381  44.7877585     1   C #> 382  35.6373159     1   C #> 383 195.7309782     1   C #> 384 359.0609929     1   C #> 385  87.9252899     1   C #> 386  63.9314490     1   C #> 387  20.1651679     1   C #> 388 328.2676133     1   C #> 389 122.3091982     1   C #> 390  76.2564232     1   C #> 391  60.3352419     1   C #> 392  46.7123059     1   C #> 393 170.2945732     1   C #> 394  53.9880957     1   C #> 395 139.9918337     1   C #> 396   4.2157213     1   C #> 397  59.6334728     1   C #> 398  62.7271303     1   C #> 399  54.7736547     1   C #> 400  25.7911074     1   C #> 401  70.5377511     1   C #> 402  90.4953554     1   C #> 403  68.7783819     1   C #> 404  33.7365728     1   C #> 405 254.1279077     1   C #> 406 236.5857254     1   C #> 407 410.6348769     1   C #> 408  23.3445397     1   C #> 409  63.9301624     1   C #> 410  49.2088476     1   C #> 411 311.1617654     1   C #> 412  66.2181329     1   C #> 413 170.5941866     1   C #> 414  93.5285471     1   C #> 415   9.4629687     1   C #> 416 165.3656360     1   C #> 417 168.8080453     1   C #> 418   5.1997817     1   C #> 419  56.5679692     1   C #> 420   6.2655921     1   C #> 421  46.0280852     1   C #> 422  20.3899054     1   C #> 423  52.2663301     1   C #> 424 395.3935200     1   C #> 425  28.0691047     1   C #> 426 130.3141560     1   C #> 427 137.2698861     1   C #> 428  38.2862049     1   C #> 429  47.6901231     1   C #> 430 138.0820810     1   C #> 431  32.1799785     1   C #> 432 112.3051926     1   C #> 433  19.9185602     1   C #> 434  76.2920091     1   C #> 435  56.6825019     1   C #> 436 179.0610637     1   C #> 437  69.1740697     1   C #> 438   2.2505426     1   C #> 439  66.6049264     1   C #> 440  26.9593252     1   C #> 441 124.2339656     1   C #> 442  49.5979148     1   C #> 443   7.1688156     1   C #> 444  86.9592721     1   C #> 445  26.6045310     1   C #> 446  43.3911009     1   C #> 447 128.9630328     1   C #> 448  80.7758703     1   C #> 449 152.6211335     1   C #> 450  46.9792384     1   C #> 451  27.2728535     1   C #> 452  53.4348720     1   C #> 453  68.2000078     1   C #> 454   2.8706938     1   C #> 455  89.2778020     1   C #> 456  73.7624779     1   C #> 457  66.9368711     1   C #> 458 137.9830289     1   C #> 459  15.6148074     1   C #> 460   2.4549901     1   C #> 461   0.4547303     1   C #> 462  61.2325835     1   C #> 463 103.7235743     1   C #> 464  37.5361795     1   C #> 465  55.5583022     1   C #> 466  11.9588032     1   C #> 467 349.9271334     1   C #> 468 197.7737516     1   C #> 469   0.4679593     1   C #> 470   6.6126079     1   C #> 471 106.1618294     1   C #> 472 338.4375734     1   C #> 473 207.4153120     1   C #> 474 206.3535025     1   C #> 475  11.8902070     1   C #> 476  83.0679504     1   C #> 477  35.3359441     1   C #> 478  26.3272993     1   C #> 479  56.3209696     1   C #> 480  86.4534772     1   C #> 481  26.0348507     1   C #> 482 129.4389274     1   C #> 483  12.6744570     1   C #> 484 211.0183539     1   C #> 485  64.4749462     1   C #> 486  40.2295100     1   C #> 487  44.0939911     1   C #> 488  57.4278961     1   C #> 489 346.9549975     1   C #> 490  29.7902965     1   C #> 491 219.5845329     1   C #> 492  16.2093773     1   C #> 493   3.5508758     1   C #> 494  14.7713947     1   C #> 495 303.9363812     1   C #> 496  16.5357401     1   C #> 497  50.8742355     1   C #> 498  94.5126584     1   C #> 499 173.5118784     1   C #> 500  17.7684122     1   C #> 501 134.5956768     1   C #> 502   3.7226013     1   C #> 503 101.2550594     1   C #> 504  82.2755429     1   C #> 505  18.2113050     1   C #> 506 102.7671387     1   C #> 507  39.3929382     1   C #> 508 102.5566076     1   C #> 509 238.8303887     1   C #> 510  32.2957137     1   C #> 511  71.5848306     1   C #> 512 217.1808990     1   C #> 513  11.5481860     1   C #> 514  50.9232784     1   C #> 515 241.0593326     1   C #> 516   0.9898505     1   C #> 517 270.9729398     1   C #> 518   0.8868238     1   C #> 519  21.4578476     1   C #> 520   5.4217727     1   C #> 521 122.4817571     1   C #> 522 130.6844099     1   C #> 523  14.5684157     1   C #> 524  15.9398174     1   C #> 525  24.3319339     1   C #> 526  38.1484547     1   C #> 527   8.6328602     1   C #> 528 216.8900398     1   C #> 529  28.7670428     1   C #> 530  57.8968761     1   C #> 531 189.0947674     1   C #> 532 195.8796317     1   C #> 533   6.7554534     1   C #> 534  55.4418422     1   C #> 535  14.9219480     1   C #> 536  56.7754443     1   C #> 537  65.9951328     1   C #> 538  59.1533520     1   C #> 539  54.5515559     1   C #> 540   0.7455647     1   C #> 541   3.5424647     1   C #> 542 213.1069769     1   C #> 543  15.2995420     1   C #> 544  62.3742666     1   C #> 545 188.2253712     1   C #> 546 109.8569379     1   C #> 547 105.3056303     1   C #> 548 181.7751578     1   C #> 549 109.8185480     1   C #> 550  58.9768111     1   C #> 551 156.6727094     1   C #> 552 123.0597689     1   C #> 553  17.1803281     1   C #> 554  12.7273790     1   C #> 555  27.2453182     1   C #> 556  19.7146427     1   C #> 557  36.6349982     1   C #> 558 272.3752120     1   C #> 559  36.3879036     1   C #> 560 146.0949964     1   C #> 561  61.0101546     1   C #> 562 360.1304361     1   C #> 563 125.1880061     1   C #> 564 181.6820019     1   C #> 565 432.3495904     1   C #> 566  59.3855396     1   C #> 567   0.9624553     1   C #> 568 149.3668701     1   C #> 569  55.9411352     1   C #> 570  73.9574770     1   C #> 571  52.3685641     1   C #> 572 280.9762834     1   C #> 573   9.9605350     1   C #> 574 168.2686229     1   C #> 575 109.0800311     1   C #> 576   1.7781866     1   C #> 577 192.0572072     1   C #> 578 121.6663948     1   C #> 579  40.9598000     1   C #> 580   4.7165329     1   C #> 581 152.1987498     1   C #> 582   2.4999375     1   C #> 583  62.8220411     1   C #> 584  33.3294665     1   C #> 585  20.3195787     1   C #> 586  59.9037111     1   C #> 587 276.1406159     1   C #> 588   1.8819531     1   C #> 589  37.0348936     1   C #> 590  23.6847101     1   C #> 591 122.1550133     1   C #> 592  69.6353349     1   C #> 593  10.1666166     1   C #> 594 308.0106026     1   C #> 595  92.5965371     1   C #> 596 207.9073549     1   C #> 597 306.9951522     1   C #> 598  29.8032770     1   C #> 599  63.5375426     1   C #> 600  60.9214087     1   C #> 601   1.8974623     1   C #> 602  38.1757393     1   C #> 603  94.0161631     1   C #> 604  68.4254251     1   C #> 605  85.0044681     1   C #> 606  28.8755003     1   C #> 607  45.4793288     1   C #> 608   3.8261500     1   C #> 609  30.1251444     1   C #> 610 179.4245793     1   C #> 611  33.8091982     1   C #> 612  98.3950780     1   C #> 613   9.8651749     1   C #> 614   6.1270467     1   C #> 615 203.2524779     1   C #> 616  80.7678195     1   C #> 617  54.2690465     1   C #> 618   5.2908184     1   C #> 619  17.6934652     1   C #> 620 105.7681953     1   C #> 621  37.9967270     1   C #> 622 128.7672423     1   C #> 623  95.2012045     1   C #> 624  75.5118328     1   C #> 625  28.2900272     1   C #> 626  29.0599836     1   C #> 627 385.0733918     1   C #> 628  75.7357239     1   C #> 629 180.0774981     1   C #> 630   0.2504356     1   C #> 631   7.4870408     1   C #> 632 109.4101567     1   C #> 633  54.4805610     1   C #> 634 150.4004485     1   C #> 635  60.2235611     1   C #> 636  79.4531928     1   C #> 637 208.6755296     1   C #> 638  41.8376173     1   C #> 639  13.2705437     1   C #> 640  42.3934947     1   C #> 641  25.5786448     1   C #> 642  24.1257563     1   C #> 643  25.6245013     1   C #> 644  42.3522825     1   C #> 645  30.3316336     1   C #> 646  64.2448360     1   C #> 647  65.2345577     1   C #> 648   2.9259080     1   C #> 649   0.7544762     1   C #> 650   9.8111783     1   C #> 651  54.5728111     1   C #> 652  14.8313343     1   C #> 653  92.4349812     1   C #> 654 206.9897115     1   C #> 655   4.3363978     1   C #> 656 126.3544321     1   C #> 657  61.1648358     1   C #> 658   7.2121233     1   C #> 659  20.1326000     1   C #> 660  97.3169365     1   C #> 661  12.3515482     1   C #> 662 125.5980355     1   C #> 663  33.0257509     1   C #> 664  52.4541262     1   C #> 665  69.8797378     1   C #> 666  20.5634573     1   C #> 667 202.0092538     1   C #> 668   0.7634996     1   C #> 669 176.1329749     1   C #> 670  25.8601898     1   C #> 671 119.4433218     1   C #> 672  50.4333857     1   C #> 673  33.3157194     1   C #> 674  36.2734314     1   C #> 675  17.3770767     1   C #> 676 333.2811826     1   C #> 677 133.1432645     1   C #> 678   5.9647760     1   C #> 679  42.6350708     1   C #> 680 260.8186123     1   C #> 681  15.7038074     1   C #> 682  61.4575576     1   C #> 683  29.6649397     1   C #> 684  88.3704071     1   C #> 685  37.8367104     1   C #> 686  44.8863824     1   C #> 687 131.1053585     1   C #> 688   4.2570066     1   C #> 689  18.2785781     1   C #> 690  46.5918226     1   C #> 691  17.9491164     1   C #> 692  22.3337852     1   C #> 693  72.4316778     1   C #> 694  51.7076510     1   C #> 695 141.0888687     1   C #> 696  26.3551823     1   C #> 697  73.9750546     1   C #> 698 224.8529753     1   C #> 699 331.4151551     1   C #> 700  33.9325202     1   C #> 701  37.8032221     1   C #> 702  10.0572560     1   C #> 703  24.1215638     1   C #> 704  26.8714586     1   C #> 705  45.3035811     1   C #> 706  28.1369080     1   C #> 707   5.2267033     1   C #> 708  99.1843533     1   C #> 709   0.9260236     1   C #> 710 141.1788408     1   C #> 711 351.1399865     1   C #> 712  22.1090153     1   C #> 713 159.1137213     1   C #> 714  38.3125332     1   C #> 715 102.4666222     1   C #> 716  22.5130915     1   C #> 717  67.3724711     1   C #> 718   5.2977519     1   C #> 719 338.2797970     1   C #> 720 324.2844532     1   C #> 721  35.1382473     1   C #> 722  13.1855325     1   C #> 723  82.1709191     1   C #> 724  35.3322755     1   C #> 725  59.9593228     1   C #> 726 132.8322791     1   C #> 727   2.3335002     1   C #> 728  34.7189146     1   C #> 729  55.7721040     1   C #> 730  72.5377629     1   C #> 731 126.0833015     1   C #> 732  82.1371958     1   C #> 733  79.8656971     1   C #> 734  91.0498106     1   C #> 735 166.6115962     1   C #> 736 176.8942873     1   C #> 737 192.1270684     1   C #> 738  39.2302592     1   C #> 739   6.3414907     1   C #> 740   3.8759611     1   C #> 741  29.9652297     1   C #> 742   6.1536886     1   C #> 743  48.7141680     1   C #> 744   0.0990051     1   C #> 745  44.6563684     1   C #> 746  58.0575307     1   C #> 747  23.2741787     1   C #> 748 139.0004481     1   C #> 749  13.9091483     1   C #> 750  40.8809036     1   C #> 751 130.0792075     1   C #> 752  35.4366759     1   C #> 753  65.6279325     1   C #> 754  14.8027962     1   C #> 755 115.5402014     1   C #> 756 147.6945953     1   C #> 757  21.0786068     1   C #> 758  12.7715724     1   C #> 759  35.7647948     1   C #> 760  16.2384083     1   C #> 761 142.5683763     1   C #> 762  48.2883192     1   C #> 763  14.4747400     1   C #> 764  70.7480995     1   C #> 765 142.5645692     1   C #> 766  20.0736197     1   C #> 767  38.4760124     1   C #> 768  72.6307495     1   C #> 769 500.0000000     0   C #> 770  58.2882059     1   C #> 771  34.5957184     1   C #> 772  24.5623167     1   C #> 773  86.6690970     1   C #> 774   1.5079031     1   C #> 775  31.2528024     1   C #> 776  32.2891538     1   C #> 777   2.0602410     1   C #> 778  45.1162448     1   C #> 779  37.6824055     1   C #> 780   4.1874008     1   C #> 781 137.1278015     1   C #> 782  49.8113560     1   C #> 783 500.0000000     0   C #> 784  46.9213552     1   C #> 785  90.4030377     1   C #> 786 400.3366689     1   C #> 787 124.6043711     1   C #> 788 104.7838825     1   C #> 789 159.0973847     1   C #> 790  78.6201401     1   C #> 791  13.5408137     1   C #> 792  19.2799127     1   C #> 793  49.8818012     1   C #> 794  52.2137377     1   C #> 795 274.4921166     1   C #> 796  16.1620879     1   C #> 797  85.2940604     1   C #> 798  36.9315347     1   C #> 799 217.8616656     1   C #> 800 139.2077015     1   C  #### prepare data target_pop <- process_agd(agd) adsl <- dummize_ipd(adsl_twt, dummize_cols = c(\"SEX\"), dummize_ref_level = c(\"Female\")) use_adsl <- center_ipd(ipd = adsl, agd = target_pop)  #### derive weights match_res <- estimate_weights(   data = use_adsl,   centered_colnames = grep(\"_CENTERED$\", names(use_adsl)),   start_val = 0,   method = \"BFGS\" )  # plot by trial kmplot(   weights_object = match_res,   tte_ipd = adtte_twt,   trt_var_ipd = \"ARM\",   tte_pseudo_ipd = pseudo_ipd_twt,   trt_var_agd = \"ARM\",   endpoint_name = \"Overall Survival\",   trt_ipd = \"A\",   trt_agd = \"B\",   trt_common = \"C\",   km_conf_type = \"log-log\",   km_layout = \"by_trial\",   time_scale = \"month\",   time_grid = seq(0, 20, by = 2),   use_colors = NULL,   use_line_types = NULL,   use_pch_cex = 0.65,   use_pch_alpha = 100 )     # plot by arm kmplot(   weights_object = match_res,   tte_ipd = adtte_twt,   trt_var_ipd = \"ARM\",   tte_pseudo_ipd = pseudo_ipd_twt,   trt_var_agd = \"ARM\",   endpoint_name = \"Overall Survival\",   trt_ipd = \"A\",   trt_agd = \"B\",   trt_common = \"C\",   km_conf_type = \"log-log\",   km_layout = \"by_arm\",   time_scale = \"month\",   time_grid = seq(0, 20, by = 2),   use_colors = NULL,   use_line_types = NULL,   use_pch_cex = 0.65,   use_pch_alpha = 100 )    # plot all kmplot(   weights_object = match_res,   tte_ipd = adtte_twt,   trt_var_ipd = \"ARM\",   tte_pseudo_ipd = pseudo_ipd_twt,   trt_var_agd = \"ARM\",   endpoint_name = \"Overall Survival\",   trt_ipd = \"A\",   trt_agd = \"B\",   trt_common = \"C\",   km_conf_type = \"log-log\",   km_layout = \"all\",   time_scale = \"month\",   time_grid = seq(0, 20, by = 2),   use_colors = NULL,   use_line_types = NULL,   use_pch_cex = 0.65,   use_pch_alpha = 100 )     # unanchored example using maicplus_kmplot  ### IPD # Read in relevant ADaM data and rename variables of interest adsl <- read.csv(system.file(\"extdata\", \"adsl.csv\",   package = \"maicplus\",   mustWork = TRUE )) adtte <- read.csv(system.file(\"extdata\", \"adtte.csv\",   package = \"maicplus\",   mustWork = TRUE )) adtte$TIME <- adtte$AVAL adtte$EVENT <- adtte$EVNT  ### AgD # Baseline aggregate data for the comparator population target_pop <- read.csv(system.file(\"extdata\", \"aggregate_data_example_1.csv\",   package = \"maicplus\", mustWork = TRUE )) # for time-to-event endpoints, pseudo IPD from digitalized KM pseudo_ipd <- read.csv(system.file(\"extdata\", \"psuedo_IPD.csv\",   package = \"maicplus\",   mustWork = TRUE )) pseudo_ipd$ARM <- \"B\"  #### prepare data target_pop <- process_agd(target_pop) adsl <- dummize_ipd(adsl, dummize_cols = c(\"SEX\"), dummize_ref_level = c(\"Female\")) use_adsl <- center_ipd(ipd = adsl, agd = target_pop)  #### derive weights match_res <- estimate_weights(   data = use_adsl,   centered_colnames = grep(\"_CENTERED$\", names(use_adsl)),   start_val = 0,   method = \"BFGS\" )  # plot kmplot(   weights_object = match_res,   tte_ipd = adtte,   trt_var_ipd = \"ARM\",   tte_pseudo_ipd = pseudo_ipd,   trt_var_agd = \"ARM\",   endpoint_name = \"Overall Survival\",   trt_ipd = \"A\",   trt_agd = \"B\",   trt_common = NULL,   km_conf_type = \"log-log\",   time_scale = \"month\",   time_grid = seq(0, 20, by = 2),   use_colors = NULL,   use_line_types = NULL,   use_pch_cex = 0.65,   use_pch_alpha = 100 )"},{"path":"https://hta-pharma.github.io/maicplus/reference/kmplot2.html","id":null,"dir":"Reference","previous_headings":"","what":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","title":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","text":"wrapper function basic_kmplot2. argument setting similar maic_anchored maic_unanchored, used two functions.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/kmplot2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","text":"","code":"kmplot2(   weights_object,   tte_ipd,   tte_pseudo_ipd,   trt_ipd,   trt_agd,   trt_common = NULL,   trt_var_ipd = \"ARM\",   trt_var_agd = \"ARM\",   km_conf_type = \"log-log\",   km_layout = c(\"all\", \"by_trial\", \"by_arm\"),   time_scale,   ... )"},{"path":"https://hta-pharma.github.io/maicplus/reference/kmplot2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","text":"weights_object object returned estimate_weight tte_ipd data frame individual patient data (IPD) internal trial, contain least \"USUBJID\", \"EVENT\", \"TIME\" columns column indicating treatment assignment tte_pseudo_ipd data frame pseudo IPD digitized KM curves external trial (time--event endpoint), contain least \"EVENT\", \"TIME\" trt_ipd string, name interested investigation arm internal trial dat_igd (real IPD) trt_agd string, name interested investigation arm external trial dat_pseudo (pseudo IPD) trt_common string, name common comparator internal external trial, default NULL, indicating unanchored case trt_var_ipd string, column name tte_ipd contains treatment assignment trt_var_agd string, column name tte_pseudo_ipd contains treatment assignment km_conf_type string, pass conf.type survfit km_layout string, applicable unanchored case (trt_common = NULL), indicated desired layout output KM curve. time_scale string, time unit median survival time, taking value 'years', 'months', weeks' 'days' ... arguments basic_kmplot2","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/kmplot2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","text":"unanchored case, KM plot risk set table. anchored case, depending km_layout, \"by_trial\", 2 1 plot, first KM curves (incl. weighted) IPD trial, KM curves AgD trial, risk set table. \"by_arm\", 2 1 plot, first KM curves trt_agd  trt_ipd (without weights), KM curves trt_common AgD trial IPD trial (without weights). Risk set table appended. \"\", 2 2 plot, plots \"by_trial\" \"by_arm\" without risk set table appended.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/kmplot2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kaplan-Meier (KM) plot function for anchored and unanchored cases using ggplot — kmplot2","text":"","code":"# anchored example using kmplot  ### IPD # Read in relevant ADaM data and rename variables of interest adsl <- read.csv(system.file(\"extdata\", \"adsl.csv\",   package = \"maicplus\",   mustWork = TRUE )) adtte <- read.csv(system.file(\"extdata\", \"adtte.csv\",   package = \"maicplus\",   mustWork = TRUE )) adtte$TIME <- adtte$AVAL adtte$EVENT <- adtte$EVNT adtte2 <- adtte adtte2$ARM <- \"C\" adtte2$TIME <- adtte2$TIME + 7 adtte <- rbind(adtte, adtte2)  ### AgD # Baseline aggregate data for the comparator population target_pop <- read.csv(system.file(\"extdata\", \"aggregate_data_example_1.csv\",   package = \"maicplus\", mustWork = TRUE )) # for time-to-event endpoints, pseudo IPD from digitalized KM pseudo_ipd <- read.csv(system.file(\"extdata\", \"psuedo_IPD.csv\",   package = \"maicplus\",   mustWork = TRUE )) pseudo_ipd$ARM <- \"B\" pseudo_ipd2 <- pseudo_ipd pseudo_ipd2$ARM <- \"C\" pseudo_ipd2$Time <- pseudo_ipd2$Time + 5 pseudo_ipd <- rbind(pseudo_ipd, pseudo_ipd2)  #### prepare data target_pop <- process_agd(target_pop) adsl <- dummize_ipd(adsl, dummize_cols = c(\"SEX\"), dummize_ref_level = c(\"Female\")) use_adsl <- center_ipd(ipd = adsl, agd = target_pop)  #### derive weights match_res <- estimate_weights(   data = use_adsl,   centered_colnames = grep(\"_CENTERED$\", names(use_adsl)),   start_val = 0,   method = \"BFGS\" )  # plot by trial kmplot2(   weights_object = match_res,   tte_ipd = adtte,   tte_pseudo_ipd = pseudo_ipd,   trt_ipd = \"A\",   trt_agd = \"B\",   trt_common = \"C\",   trt_var_ipd = \"ARM\",   trt_var_agd = \"ARM\",   endpoint_name = \"Overall Survival\",   km_conf_type = \"log-log\",   km_layout = \"by_trial\",   time_scale = \"month\",   break_x_by = 2 ) #> Warning: There was 1 warning in `mutate()`. #> ℹ In argument: `survtable = purrr::map2(...)`. #> Caused by warning: #> ! `select_()` was deprecated in dplyr 0.7.0. #> ℹ Please use `select()` instead. #> ℹ The deprecated feature was likely used in the survminer package. #>   Please report the issue at <https://github.com/kassambara/survminer/issues>.   # plot by arm kmplot2(   weights_object = match_res,   tte_ipd = adtte,   tte_pseudo_ipd = pseudo_ipd,   trt_ipd = \"A\",   trt_agd = \"B\",   trt_common = \"C\",   trt_var_ipd = \"ARM\",   trt_var_agd = \"ARM\",   endpoint_name = \"Overall Survival\",   km_conf_type = \"log-log\",   km_layout = \"by_arm\",   time_scale = \"month\",   break_x_by = 2 )   # plot all kmplot2(   weights_object = match_res,   tte_ipd = adtte,   tte_pseudo_ipd = pseudo_ipd,   trt_ipd = \"A\",   trt_agd = \"B\",   trt_common = \"C\",   trt_var_ipd = \"ARM\",   trt_var_agd = \"ARM\",   endpoint_name = \"Overall Survival\",   km_conf_type = \"log-log\",   km_layout = \"all\",   time_scale = \"month\",   break_x_by = 2,   xlim = c(0, 20) )  # unanchored example using maicplus_kmplot  ### IPD # Read in relevant ADaM data and rename variables of interest adsl <- read.csv(system.file(\"extdata\", \"adsl.csv\",   package = \"maicplus\",   mustWork = TRUE )) adtte <- read.csv(system.file(\"extdata\", \"adtte.csv\",   package = \"maicplus\",   mustWork = TRUE )) adtte$TIME <- adtte$AVAL adtte$EVENT <- adtte$EVNT  ### AgD # Baseline aggregate data for the comparator population target_pop <- read.csv(system.file(\"extdata\", \"aggregate_data_example_1.csv\",   package = \"maicplus\", mustWork = TRUE )) # for time-to-event endpoints, pseudo IPD from digitalized KM pseudo_ipd <- read.csv(system.file(\"extdata\", \"psuedo_IPD.csv\",   package = \"maicplus\",   mustWork = TRUE )) pseudo_ipd$ARM <- \"B\"  #### prepare data target_pop <- process_agd(target_pop) adsl <- dummize_ipd(adsl, dummize_cols = c(\"SEX\"), dummize_ref_level = c(\"Female\")) use_adsl <- center_ipd(ipd = adsl, agd = target_pop)  #### derive weights match_res <- estimate_weights(   data = use_adsl,   centered_colnames = grep(\"_CENTERED$\", names(use_adsl)),   start_val = 0,   method = \"BFGS\" )   kmplot2(   weights_object = match_res,   tte_ipd = adtte,   tte_pseudo_ipd = pseudo_ipd,   trt_ipd = \"A\",   trt_agd = \"B\",   trt_common = NULL,   trt_var_ipd = \"ARM\",   trt_var_agd = \"ARM\",   endpoint_name = \"Overall Survival\",   km_conf_type = \"log-log\",   time_scale = \"month\",   break_x_by = 2,   xlim = c(0, 20) )"},{"path":"https://hta-pharma.github.io/maicplus/reference/maic_anchored.html","id":null,"dir":"Reference","previous_headings":"","what":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","title":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","text":"wrapper function provide adjusted effect estimates relevant statistics anchored case (.e. common comparator arm internal external trial).","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/maic_anchored.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","text":"","code":"maic_anchored(   weights_object,   ipd,   pseudo_ipd,   trt_ipd,   trt_agd,   trt_common,   trt_var_ipd = \"ARM\",   trt_var_agd = \"ARM\",   endpoint_type = \"tte\",   endpoint_name = \"Time to Event Endpoint\",   eff_measure = c(\"HR\", \"OR\", \"RR\", \"RD\", \"Diff\"),   time_scale = \"months\",   km_conf_type = \"log-log\",   boot_ci_type = c(\"norm\", \"basic\", \"stud\", \"perc\", \"bca\") )"},{"path":"https://hta-pharma.github.io/maicplus/reference/maic_anchored.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","text":"weights_object object returned estimate_weight ipd data frame meet format requirements 'Details', individual patient data (IPD) internal trial pseudo_ipd data frame, pseudo IPD digitized KM curve external trial (time--event endpoint) contingency table (binary endpoint) trt_ipd string, name interested investigation arm internal trial ipd (internal IPD) trt_agd string, name interested investigation arm external trial pseudo_ipd (pseudo IPD) trt_common string, name common comparator internal external trial trt_var_ipd string, column name ipd contains treatment assignment trt_var_agd string, column name ipd contains treatment assignment endpoint_type string, one following \"binary\", \"tte\" (time event) endpoint_name string, name time event endpoint, show last line title eff_measure string, \"RD\" (risk difference), \"\" (odds ratio), \"RR\" (relative risk) binary endpoint; \"HR\" time--event endpoint. default NULL, \"\" used binary case, otherwise \"HR\" used. time_scale string, time unit median survival time, taking value 'years', 'months', 'weeks' 'days'. NOTE: assumed values TIME column ipd pseudo_ipd unit days km_conf_type string, pass conf.type survfit boot_ci_type string, one c(\"norm\",\"basic\", \"stud\", \"perc\", \"bca\") select type bootstrap confidence interval. See boot::boot.ci details.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/maic_anchored.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","text":"list, contains 'descriptive' 'inferential'","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/maic_anchored.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Anchored MAIC for binary and time-to-event endpoint — maic_anchored","text":"time--event analysis, required input ipd pseudo_ipd following columns. function sensitive upper lower case letters column names. USUBJID - character, unique subject ID ARM - character factor, treatment indicator, column name 'ARM'. User specify trt_var_ipd trt_var_agd EVENT - numeric, 1 censored/death, 0 otherwise TIME - numeric column, observation time EVENT; unit days","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/maic_unanchored.html","id":null,"dir":"Reference","previous_headings":"","what":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","title":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","text":"wrapper function provide adjusted effect estimates relevant statistics unanchored case (.e. common comparator arm internal external trial).","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/maic_unanchored.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","text":"","code":"maic_unanchored(   weights_object,   ipd,   pseudo_ipd,   trt_ipd,   trt_agd,   trt_var_ipd = \"ARM\",   trt_var_agd = \"ARM\",   endpoint_type = \"tte\",   endpoint_name = \"Time to Event Endpoint\",   eff_measure = c(\"HR\", \"OR\", \"RR\", \"RD\"),   boot_ci_type = c(\"norm\", \"basic\", \"stud\", \"perc\", \"bca\"),   time_scale = \"months\",   km_conf_type = \"log-log\",   binary_robust_cov_type = \"HC3\" )"},{"path":"https://hta-pharma.github.io/maicplus/reference/maic_unanchored.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","text":"weights_object object returned estimate_weight ipd data frame meet format requirements 'Details', individual patient data (IPD) internal trial pseudo_ipd data frame, pseudo IPD digitized KM curve external trial (time--event endpoint) contingency table (binary endpoint) trt_ipd string, name interested investigation arm internal trial dat_igd (real IPD) trt_agd string, name interested investigation arm external trial pseudo_ipd (pseudo IPD) trt_var_ipd string, column name ipd contains treatment assignment trt_var_agd string, column name ipd contains treatment assignment endpoint_type string, one following \"binary\", \"tte\" (time event) endpoint_name string, name time event endpoint, show last line title eff_measure string, \"RD\" (risk difference), \"\" (odds ratio), \"RR\" (relative risk) binary endpoint; \"HR\" time--event endpoint. default NULL, \"\" used binary case, otherwise \"HR\" used. boot_ci_type string, one c(\"norm\",\"basic\", \"stud\", \"perc\", \"bca\") select type bootstrap confidence interval. See boot::boot.ci details. time_scale string, time unit median survival time, taking value 'years', 'months', 'weeks' 'days'. NOTE: assumed values TIME column ipd pseudo_ipd unit days km_conf_type string, pass conf.type survfit binary_robust_cov_type string pass argument type sandwich::vcovHC, see possible options documentation function. Default \"HC3\"","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/maic_unanchored.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","text":"list, contains 'descriptive' 'inferential'","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/maic_unanchored.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Unanchored MAIC for binary and time-to-event endpoint — maic_unanchored","text":"time--event analysis, required input ipd pseudo_ipd following columns. function sensitive upper lower case letters column names. USUBJID - character, unique subject ID ARM - character factor, treatment indicator, column name 'ARM'. User specify trt_var_ipd trt_var_agd EVENT - numeric, 1 censored/death, 0 otherwise TIME - numeric column, observation time EVENT; unit days","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/maicplus-package.html","id":null,"dir":"Reference","previous_headings":"","what":"maicplus: Matching Adjusted Indirect Comparison — maicplus-package","title":"maicplus: Matching Adjusted Indirect Comparison — maicplus-package","text":"maicplus package facilitates performing matching adjusted indirect comparison (MAIC) analysis endpoint interest either time--event (e.g. overall survival) binary (e.g. objective tumor response).","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/reference/maicplus-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"maicplus: Matching Adjusted Indirect Comparison — maicplus-package","text":"Maintainer: hta-pharma hta-pharma@example.com","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/medSurv_makeup.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","title":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","text":"Extract display median survival time confidence interval","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/medSurv_makeup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","text":"","code":"medSurv_makeup(km_fit, legend = \"before matching\", time_scale)"},{"path":"https://hta-pharma.github.io/maicplus/reference/medSurv_makeup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","text":"km_fit returned object survival::survfit legend character string, name used 'type' column returned data frame time_scale character string, 'years', 'months', 'weeks' 'days', time unit median survival time","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/medSurv_makeup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","text":"data frame index column 'type', median survival time confidence interval","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/medSurv_makeup.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to retrieve median survival time from a survival::survfit object — medSurv_makeup","text":"","code":"library(survival) load(system.file(\"extdata\", \"combined_data_tte.rda\", package = \"maicplus\", mustWork = TRUE)) kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data_tte, conf.type = \"log-log\") kmobj_adj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data_tte,   weights = combined_data_tte$weights, conf.type = \"log-log\" )  # Derive median survival time medSurv <- medSurv_makeup(kmobj, legend = \"before matching\", time_scale = \"day\") medSurv_adj <- medSurv_makeup(kmobj_adj, legend = \"after matching\", time_scale = \"day\") medSurv_out <- rbind(medSurv, medSurv_adj) medSurv_out #>   treatment            type records    n.max  n.start    events    rmean #> 1     ARM=A before matching     500 500.0000 500.0000 190.00000 265.1012 #> 2     ARM=B before matching     300 300.0000 300.0000 178.00000 130.9893 #> 3     ARM=A  after matching     500 199.8422 199.8422  66.84953 307.7223 #> 4     ARM=B  after matching     300 300.0000 300.0000 178.00000 130.9893 #>   se(rmean)    median   0.95LCL  0.95UCL #> 1  10.80981 230.94839 191.10767 313.1574 #> 2  10.24910  83.58535  68.82298 101.0786 #> 3  16.71338 362.20670 237.05932 452.7209 #> 4  10.24910  83.58535  68.82298 101.0786"},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnosis plot of proportional hazard assumption for anchored and unanchored — ph_diagplot","title":"Diagnosis plot of proportional hazard assumption for anchored and unanchored — ph_diagplot","text":"Diagnosis plot proportional hazard assumption anchored unanchored","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnosis plot of proportional hazard assumption for anchored and unanchored — ph_diagplot","text":"","code":"ph_diagplot(   weights_object,   tte_ipd,   tte_pseudo_ipd,   trt_ipd,   trt_agd,   trt_common = NULL,   trt_var_ipd = \"ARM\",   trt_var_agd = \"ARM\",   endpoint_name = \"Time to Event Endpoint\",   time_scale,   zph_transform = \"log\",   zph_log_hazard = TRUE )"},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnosis plot of proportional hazard assumption for anchored and unanchored — ph_diagplot","text":"weights_object object returned estimate_weight tte_ipd data frame individual patient data (IPD) internal trial, contain least \"USUBJID\", \"EVENT\", \"TIME\" columns column indicating treatment assignment tte_pseudo_ipd data frame pseudo IPD digitized KM curves external trial (time--event endpoint), contain least \"EVENT\", \"TIME\" trt_ipd string, name interested investigation arm internal trial dat_igd (real IPD) trt_agd string, name interested investigation arm external trial dat_pseudo (pseudo IPD) trt_common string, name common comparator internal external trial, default NULL, indicating unanchored case trt_var_ipd string, column name dat_ipd contains treatment assignment trt_var_agd string, column name dat_ipd contains treatment assignment endpoint_name string, name time event endpoint, show last line title time_scale string, time unit median survival time, taking value 'years', 'months', 'weeks' 'days' zph_transform string, pass survival::cox.zph, default \"log\" zph_log_hazard logical, TRUE (default), y axis time dependent hazard function log-hazard, otherwise, hazard.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnosis plot of proportional hazard assumption for anchored and unanchored — ph_diagplot","text":"3 2 plot, include log-cumulative hazard plot, time dependent hazard function unscaled Schoenfeld residual plot, matching","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot_lch.html","id":null,"dir":"Reference","previous_headings":"","what":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"plot also known log negative log survival rate.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot_lch.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"","code":"ph_diagplot_lch(   km_fit,   time_scale,   log_time = TRUE,   endpoint_name = \"\",   subtitle = \"\",   exclude_censor = TRUE )"},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot_lch.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"km_fit returned object survival::survfit time_scale character string, 'years', 'months', 'weeks' 'days', time unit median survival time log_time logical, TRUE (default) FALSE endpoint_name character string, name endpoint subtitle character string, subtitle plot exclude_censor logical, censored data point plotted","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot_lch.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"plot log cumulative hazard rate","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot_lch.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"diagnosis plot proportional hazard assumption, versus log-time (default) time","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot_lch.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PH Diagnosis Plot of Log Cumulative Hazard Rate versus time or log-time — ph_diagplot_lch","text":"","code":"library(survival) load(system.file(\"extdata\", \"combined_data_tte.rda\", package = \"maicplus\", mustWork = TRUE)) kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data_tte, conf.type = \"log-log\") ph_diagplot_lch(kmobj,   time_scale = \"month\", log_time = TRUE,   endpoint_name = \"OS\", subtitle = \"(Before Matching)\" )"},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot_schoenfeld.html","id":null,"dir":"Reference","previous_headings":"","what":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","title":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","text":"PH Diagnosis Plot Schoenfeld residuals Cox model fit","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot_schoenfeld.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","text":"","code":"ph_diagplot_schoenfeld(   coxobj,   time_scale = \"months\",   log_time = TRUE,   endpoint_name = \"\",   subtitle = \"\" )"},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot_schoenfeld.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","text":"coxobj object returned coxph time_scale character string, 'years', 'months', 'weeks' 'days', time unit median survival time log_time logical, TRUE (default) FALSE endpoint_name character string, name endpoint subtitle character string, subtitle plot","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot_schoenfeld.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","text":"plot Schoenfeld residuals","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/ph_diagplot_schoenfeld.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PH Diagnosis Plot of Schoenfeld residuals for a Cox model fit — ph_diagplot_schoenfeld","text":"","code":"library(survival) load(system.file(\"extdata\", \"combined_data_tte.rda\", package = \"maicplus\", mustWork = TRUE)) unweighted_cox <- coxph(Surv(TIME, EVENT == 1) ~ ARM, data = combined_data_tte) ph_diagplot_schoenfeld(unweighted_cox,   time_scale = \"month\", log_time = TRUE,   endpoint_name = \"OS\", subtitle = \"(Before Matching)\" )"},{"path":"https://hta-pharma.github.io/maicplus/reference/plot_weights_base.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot MAIC weights in a histogram with key statistics in legend — plot_weights_base","title":"Plot MAIC weights in a histogram with key statistics in legend — plot_weights_base","text":"Generates base R histogram weights. Default plot either unscaled scaled weights .","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/plot_weights_base.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot MAIC weights in a histogram with key statistics in legend — plot_weights_base","text":"","code":"plot_weights_base(   weighted_data,   bin_col,   vline_col,   main_title,   scaled_weights )"},{"path":"https://hta-pharma.github.io/maicplus/reference/plot_weights_base.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot MAIC weights in a histogram with key statistics in legend — plot_weights_base","text":"weighted_data object returned calculating weights using estimate_weights bin_col string, color bins histogram vline_col string, color vertical line histogram main_title title plot scaled_weights indicator using scaled weights instead regular weights","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/plot_weights_base.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot MAIC weights in a histogram with key statistics in legend — plot_weights_base","text":"plot unscaled scaled weights","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/plot_weights_ggplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot MAIC weights in a histogram with key statistics in legend using ggplot2 — plot_weights_ggplot","title":"Plot MAIC weights in a histogram with key statistics in legend using ggplot2 — plot_weights_ggplot","text":"Generates ggplot histogram weights. Default plot unscaled scaled weights graph.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/plot_weights_ggplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot MAIC weights in a histogram with key statistics in legend using ggplot2 — plot_weights_ggplot","text":"","code":"plot_weights_ggplot(weighted_data, bin_col, vline_col, main_title, bins)"},{"path":"https://hta-pharma.github.io/maicplus/reference/plot_weights_ggplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot MAIC weights in a histogram with key statistics in legend using ggplot2 — plot_weights_ggplot","text":"weighted_data object returned calculating weights using estimate_weights bin_col string, color bins histogram vline_col string, color vertical line histogram main_title Name scaled weights plot unscaled weights plot, respectively. bins number bin parameter use","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/plot_weights_ggplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot MAIC weights in a histogram with key statistics in legend using ggplot2 — plot_weights_ggplot","text":"plot unscaled scaled weights","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/process_agd.html","id":null,"dir":"Reference","previous_headings":"","what":"Pre-process aggregate data — process_agd","title":"Pre-process aggregate data — process_agd","text":"function checks format aggregate data. Data required three columns: STUDY, ARM, N. Column names legal suffixes (MEAN, MEDIAN, SD, COUNT, PROP) dropped. variable count variable, converted proportions dividing sample size (N). Note, count specified, proportion always calculated based count, , specified proportion ignored applicable. aggregated data comes multiple sources (.e. different analysis population) sample size differs variable, one option specify proportion directly instead count using suffix _PROP.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/process_agd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pre-process aggregate data — process_agd","text":"","code":"process_agd(raw_agd)"},{"path":"https://hta-pharma.github.io/maicplus/reference/process_agd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pre-process aggregate data — process_agd","text":"raw_agd raw aggregate data contain STUDY, ARM, N. Variable names followed legal suffixes (.e. MEAN, MEDIAN, SD, COUNT, PROP).","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/process_agd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pre-process aggregate data — process_agd","text":"pre-processed aggregate level data","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/process_agd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pre-process aggregate data — process_agd","text":"","code":"target_pop <- read.csv(system.file(\"extdata\", \"aggregate_data_example_1.csv\",   package = \"maicplus\", mustWork = TRUE )) target_pop2 <- read.csv(system.file(\"extdata\", \"aggregate_data_example_2.csv\",   package = \"maicplus\", mustWork = TRUE )) target_pop3 <- read.csv(system.file(\"extdata\", \"aggregate_data_example_3.csv\",   package = \"maicplus\", mustWork = TRUE ))  target_pop <- process_agd(target_pop) target_pop2 <- process_agd(target_pop2) target_pop3 <- process_agd(target_pop3)"},{"path":"https://hta-pharma.github.io/maicplus/reference/pseudo_ipd_sat.html","id":null,"dir":"Reference","previous_headings":"","what":"Pseudo individual patient survival data from published study — pseudo_ipd_sat","title":"Pseudo individual patient survival data from published study — pseudo_ipd_sat","text":"Pseudo individual patient survival data published study","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/pseudo_ipd_sat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pseudo individual patient survival data from published study — pseudo_ipd_sat","text":"","code":"pseudo_ipd_sat"},{"path":"https://hta-pharma.github.io/maicplus/reference/pseudo_ipd_sat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pseudo individual patient survival data from published study — pseudo_ipd_sat","text":"data frame 300 rows 3 columns: Time Survival time days. Event Event indicator 0/1. ARM Assigned treatment arm, \"B\".","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/reference/pseudo_ipd_twt.html","id":null,"dir":"Reference","previous_headings":"","what":"Pseudo individual patient survival data from published two arm study — pseudo_ipd_twt","title":"Pseudo individual patient survival data from published two arm study — pseudo_ipd_twt","text":"Pseudo individual patient survival data published two arm study","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/pseudo_ipd_twt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pseudo individual patient survival data from published two arm study — pseudo_ipd_twt","text":"","code":"pseudo_ipd_twt"},{"path":"https://hta-pharma.github.io/maicplus/reference/pseudo_ipd_twt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Pseudo individual patient survival data from published two arm study — pseudo_ipd_twt","text":"data frame 800 rows 3 columns: Time Survival time days. Event Event indicator 0/1. ARM Assigned treatment arm, \"B\", \"C\".","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/reference/report_table_binary.html","id":null,"dir":"Reference","previous_headings":"","what":"helper function: sort out a nice report table to summarize binary analysis results — report_table_binary","title":"helper function: sort out a nice report table to summarize binary analysis results — report_table_binary","text":"helper function: sort nice report table summarize binary analysis results","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/report_table_binary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"helper function: sort out a nice report table to summarize binary analysis results — report_table_binary","text":"","code":"report_table_binary(   binobj,   weighted_result = NULL,   eff_measure = c(\"OR\", \"RD\", \"RR\"),   tag = NULL )"},{"path":"https://hta-pharma.github.io/maicplus/reference/report_table_binary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"helper function: sort out a nice report table to summarize binary analysis results — report_table_binary","text":"binobj object glm() weighted_result object res_AB eff_measure string, binary effect measure, \"\", \"RR\", \"RD\" tag string, default NULL, specified, extra 1st column created output","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/report_table_binary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"helper function: sort out a nice report table to summarize binary analysis results — report_table_binary","text":"data frame sample size, incidence rate, estimate binary effect measure 95% CI Wald test hazard ratio","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/report_table_tte.html","id":null,"dir":"Reference","previous_headings":"","what":"helper function: sort out a nice report table to summarize survival analysis results — report_table_tte","title":"helper function: sort out a nice report table to summarize survival analysis results — report_table_tte","text":"helper function: sort nice report table summarize survival analysis results","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/report_table_tte.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"helper function: sort out a nice report table to summarize survival analysis results — report_table_tte","text":"","code":"report_table_tte(coxobj, medSurvobj, tag = NULL)"},{"path":"https://hta-pharma.github.io/maicplus/reference/report_table_tte.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"helper function: sort out a nice report table to summarize survival analysis results — report_table_tte","text":"coxobj returned object coxph medSurvobj returned object medSurv_makeup tag string, default NULL, specified, extra 1st column created output","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/report_table_tte.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"helper function: sort out a nice report table to summarize survival analysis results — report_table_tte","text":"data frame sample size, incidence rate, median survival time 95% CI, hazard ratio estimate 95% CI Wald test hazard ratio","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/survfit_makeup.html","id":null,"dir":"Reference","previous_headings":"","what":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","title":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","text":"Helper function select set variables used Kaplan-Meier plot","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/survfit_makeup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","text":"","code":"survfit_makeup(km_fit, single_trt_name = \"treatment\")"},{"path":"https://hta-pharma.github.io/maicplus/reference/survfit_makeup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","text":"km_fit returned object survival::survfit single_trt_name name treatment strata specified km_fit","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/survfit_makeup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","text":"list data frames variables survival::survfit(). Data frame divided treatment.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/survfit_makeup.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helper function to select set of variables used for Kaplan-Meier plot — survfit_makeup","text":"","code":"if (FALSE) { load(system.file(\"extdata\", \"combined_data_tte.rda\", package = \"maicplus\", mustWork = TRUE)) kmobj <- survfit(Surv(TIME, EVENT) ~ ARM, combined_data_tte, conf.type = \"log-log\") survfit_makeup(kmobj) }"},{"path":"https://hta-pharma.github.io/maicplus/reference/time_conversion.html","id":null,"dir":"Reference","previous_headings":"","what":"Get and Set Time Conversion Factors — set_time_conversion","title":"Get and Set Time Conversion Factors — set_time_conversion","text":"Get Set Time Conversion Factors","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/time_conversion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get and Set Time Conversion Factors — set_time_conversion","text":"","code":"set_time_conversion(days = 1, weeks = 7, months = 365.25/12, years = 365.25)  get_time_conversion(factor = c(\"days\", \"weeks\", \"months\", \"years\"))"},{"path":"https://hta-pharma.github.io/maicplus/reference/time_conversion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get and Set Time Conversion Factors — set_time_conversion","text":"days Factor divide data time units get time days weeks Factor divide data time units get time weeks months Factor divide data time units get time months years Factor divide data time units get time years factor Time factor get.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/time_conversion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get and Set Time Conversion Factors — set_time_conversion","text":"value returned. Conversion factors stored internally used within functions.","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/time_conversion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get and Set Time Conversion Factors — set_time_conversion","text":"","code":"# Native time format is years set_time_conversion(days = 1 / 365.25, weeks = 1 / 52.17857, months = 1 / 12, years = 1)  # Native time format is days set_time_conversion(days = 1, weeks = 7, months = 365.25 / 12, years = 365.25) # Get time scale factors: get_time_conversion(\"years\") #>  years  #> 365.25  get_time_conversion(\"weeks\") #> weeks  #>     7"},{"path":"https://hta-pharma.github.io/maicplus/reference/weighted_sat.html","id":null,"dir":"Reference","previous_headings":"","what":"Weighted object for single arm trial data — weighted_sat","title":"Weighted object for single arm trial data — weighted_sat","text":"Weighted object single arm trial data","code":""},{"path":"https://hta-pharma.github.io/maicplus/reference/weighted_sat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Weighted object for single arm trial data — weighted_sat","text":"","code":"weighted_sat"},{"path":"https://hta-pharma.github.io/maicplus/reference/weighted_sat.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Weighted object for single arm trial data — weighted_sat","text":"maicplus_estimate_weights object created estimate_weights() containing data patient level data weights centered_colnames Columns used MAIC nr_missing Number observations missing data ess Expected sample size opt Information optim weight calculation boot Parameters bootstrap sample weights, NULL object","code":""},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/reference/weighted_twt.html","id":null,"dir":"Reference","previous_headings":"","what":"Weighted object for two arm trial data — weighted_twt","title":"Weighted object for two arm trial data — weighted_twt","text":"weighted patient data two arm trial generated centered patient data (centered_ipd_twt). weights calculated 100 bootstrap samples. object generated using following code:","code":"estimate_weights(   data = centered_ipd_twt,   centered_colnames = c(     \"AGE_CENTERED\",     \"AGE_MEDIAN_CENTERED\",     \"AGE_SQUARED_CENTERED\",     \"SEX_MALE_CENTERED\",     \"ECOG0_CENTERED\",     \"SMOKE_CENTERED\"     ),   n_boot_iteration = 100  )"},{"path":"https://hta-pharma.github.io/maicplus/reference/weighted_twt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Weighted object for two arm trial data — weighted_twt","text":"","code":"weighted_twt"},{"path":"https://hta-pharma.github.io/maicplus/reference/weighted_twt.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Weighted object for two arm trial data — weighted_twt","text":"maicplus_estimate_weights object created estimate_weights() containing data patient level data weights centered_colnames Columns used MAIC nr_missing Number observations missing data ess Expected sample size opt Information optim weight calculation boot Parameters bootstrap sample weights 100 samples","code":""},{"path":[]},{"path":[]},{"path":"https://hta-pharma.github.io/maicplus/news/index.html","id":"new-features-0-1-0","dir":"Changelog","previous_headings":"","what":"New features","title":"maicplus 0.1.0","text":"Add initializer script.","code":""},{"path":"https://hta-pharma.github.io/maicplus/news/index.html","id":"enhancements-0-1-0","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"maicplus 0.1.0","text":"Documentation use initialize package.","code":""},{"path":"https://hta-pharma.github.io/maicplus/news/index.html","id":"bug-fixes-0-1-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"maicplus 0.1.0","text":"None.","code":""}]
diff --git a/main/sitemap.xml b/main/sitemap.xml
index 69ef6b3e..a7ad6346 100644
--- a/main/sitemap.xml
+++ b/main/sitemap.xml
@@ -153,4 +153,7 @@
   <url>
     <loc>https://hta-pharma.github.io/maicplus/reference/weighted_sat.html</loc>
   </url>
+  <url>
+    <loc>https://hta-pharma.github.io/maicplus/reference/weighted_twt.html</loc>
+  </url>
 </urlset>