add tests and update readme

R/onco_resp.R

@@ -59,7 +59,7 @@
 #' 5. Set to not estimable (NE) if:
 #'    - there is at least one CR, PR, SD, NE.
 #'
-#' After the previous steps, select the best one using the order 'CR>PR>SD>PD>NE'
+#' After executing the previous steps, select the best one using the order 'CR>PR>SD>PD>NE'
 #' for each subject. If the BOR is not unique, the first one (based on ADT) is selected.
 #'
 #' @return
@@ -95,13 +95,13 @@
 #'   "7",      "2020-02-02", "2020-04-01", "NE",
 #'   "8",      "2020-02-01", "2020-02-16", "PD"
 #' ) %>%
-#'   mutate(
-#'     ADT = ymd(ADTC),
-#'     TRTSDT = ymd(TRTSDTC),
+#'   dplyr::mutate(
+#'     ADT = lubridate::ymd(ADTC),
+#'     TRTSDT = lubridate::ymd(TRTSDTC),
 #'     PARAMCD = "OVR",
 #'     PARAM = "Overall Response by Investigator"
 #'   ) %>%
-#'   select(-TRTSDTC)
+#'   dplyr::select(-TRTSDTC)
 #'
 #' # Derive BOR without confirmation.
 #' derive_bor(data = adrs)
@@ -126,7 +126,8 @@ derive_bor <- function(data,
   rs <- if (spec_date == "pd_date") {
     data %>%
       group_by(!!sym(unique_id)) %>%
-      filter(row_number() <= match("PD", AVALC) | all(AVALC != "PD"))
+      filter(row_number() <= match("PD", AVALC) | all(AVALC != "PD")) %>%
+      ungroup()
   } else {
     data
   }
@@ -142,20 +143,20 @@ derive_bor <- function(data,
         )
       ) %>%
       left_join(aval_map, by = c("AVALC" = "avalc_temp")) %>%
-      mutate(AVAL = aval_temp) %>%
+      mutate(AVAL = .data$aval_temp) %>%
       arrange(!!sym(unique_id), AVAL, ADT) %>%
       # select the best and first one as the best overall response.
-      distinct(USUBJID, .keep_all = TRUE) %>%
+      distinct(!!sym(unique_id), .keep_all = TRUE) %>%
       mutate(
         PARAMCD = "BOR",
         PARAM = "Best Overall Response"
       ) %>%
-      select(-c(avalc_temp, aval_temp))
+      select(-c("avalc_temp", "aval_temp"))
   } else {
     conf_cr <- rs %>%
       left_join(
-        select(rs, !!sym(unique_id), ADT, AVALC),
-        by = "USUBJID", relationship = "many-to-many"
+        select(rs, !!sym(unique_id), "ADT", "AVALC"),
+        by = unique_id, relationship = "many-to-many"
       ) %>%
       # keep the joined observations are after the original observations (e.g. AVALC.x)
       filter(AVALC.x == "CR" & ADT.y > ADT.x) %>%
@@ -165,7 +166,7 @@ derive_bor <- function(data,
       ) %>%
       group_by(!!sym(unique_id)) %>%
       filter(
-        row_number() <= min(match(TRUE, flag))
+        row_number() <= min(match(TRUE, .data$flag))
       ) %>%
       # define how many NE and which responses can be acceptable between these two assessments.
       filter(sum(AVALC.y == "NE") <= max_ne & all(AVALC.y %in% c("CR", "NE"))) %>%
@@ -179,16 +180,16 @@ derive_bor <- function(data,
 
     conf_pr <- rs %>%
       left_join(
-        select(rs, !!sym(unique_id), ADT, AVALC),
-        by = "USUBJID", relationship = "many-to-many"
+        select(rs, !!sym(unique_id), "ADT", "AVALC"),
+        by = unique_id, relationship = "many-to-many"
       ) %>%
       filter(AVALC.x == "PR" & ADT.y > ADT.x) %>%
       mutate(
         flag = AVALC.y %in% c("CR", "PR") & ADT.y >= ADT.x + days(ref_interval)
       ) %>%
       group_by(!!sym(unique_id)) %>%
       filter(
-        row_number() <= min(match(TRUE, flag))
+        row_number() <= min(match(TRUE, .data$flag))
       ) %>%
       filter(sum(AVALC.y == "NE") <= max_ne & all(AVALC.y %in% c("CR", "PR", "NE"))) %>%
       ungroup() %>%
@@ -203,19 +204,19 @@ derive_bor <- function(data,
       # CR, PR and SD can be considered as SD that occurs at least 28 days after treatment start.
       filter(AVALC %in% c("CR", "PR", "SD") &
         ADT >= !!sym(ref_date) + days(ref_start_window)) %>%
-      distinct(USUBJID, .keep_all = TRUE) %>%
+      distinct(!!sym(unique_id), .keep_all = TRUE) %>%
       mutate(AVALC = "SD") %>%
       select(names(rs))
 
     pd <- rs %>%
       filter(AVALC == "PD") %>%
-      distinct(USUBJID, .keep_all = TRUE) %>%
+      distinct(!!sym(unique_id), .keep_all = TRUE) %>%
       mutate(AVALC = "PD") %>%
       select(names(rs))
 
     ne <- rs %>%
       filter(AVALC %in% c("CR", "PR", "SD", "NE")) %>%
-      distinct(USUBJID, .keep_all = TRUE) %>%
+      distinct(!!sym(unique_id), .keep_all = TRUE) %>%
       mutate(AVALC = "NE") %>%
       select(names(rs))
 
@@ -224,13 +225,13 @@ derive_bor <- function(data,
     ) %>%
       left_join(aval_map, by = c("AVALC" = "avalc_temp")) %>%
       mutate(
-        AVAL = aval_temp,
+        AVAL = .data$aval_temp,
         PARAMCD = "CBOR",
         PARAM = "Confirmed Best Overall Response"
       ) %>%
       arrange(!!sym(unique_id), AVAL, ADT) %>%
-      distinct(USUBJID, .keep_all = TRUE) %>%
-      select(-aval_temp)
+      distinct(!!sym(unique_id), .keep_all = TRUE) %>%
+      select(-"aval_temp")
   }
 
   bor_res

R/package.R

@@ -14,6 +14,10 @@
 #' @importFrom lubridate ymd days
 NULL
 
+utils::globalVariables(c(
+  "ADT", "ADT.x", "ADT.y", "AVAL", "AVALC", "AVALC.x", "AVALC.y", "."
+))
+
 .onLoad <- function(libname, pkgname) {
   op <- options()
   op.stabiot <- list(

README.Rmd

@@ -19,19 +19,17 @@ knitr::opts_chunk$set(
 [![R-CMD-check](https://github.com/kaigu1990/stabiot/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/kaigu1990/stabiot/actions/workflows/R-CMD-check.yaml)
 <!-- badges: end -->
 
-The goal of `stabiot` is to assist statisticians and programmers in using R functions 
-and methods to oversee the outputs often produced by SAS from vendors in clinical 
+The goal of `stabiot` is to assist statisticians and statistical programmers in using R functions and methods to oversee the outputs produced by SAS from outsourcing in clinical 
 trials. The data sets would be ADaM format preferably, but they do not have to 
-follow the CDISC standard.
+strictly follow the CDISC standards.
 
-In order to ensure the quality and accuracy of the results, I prefer to wrap mature 
-R package rather than rebuild the statistical methods. For now the completed sections 
-are listed as shown below.
+To guarantee accurate results, I prefer to wrap mature R package rather than rebuild statistical methods. For present, the completed sections are listed below.
 
 - Simulation of sample size determination by Bayesian.
 - Summarize Least-squares Means from models, such as ANCOVA and MMRM.
-- Computing response rate, odds ratio with or without stratification, and corresponding 
+- Compute response rate, odds ratio with or without stratification, and corresponding 
 confidence interval.
+- Derive best overall response (confirmed or not confirmed BOR) per RECIST 1.1
 
 ## Installation
 

README.md

@@ -8,19 +8,22 @@
 [![R-CMD-check](https://github.com/kaigu1990/stabiot/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/kaigu1990/stabiot/actions/workflows/R-CMD-check.yaml)
 <!-- badges: end -->
 
-The goal of `stabiot` is to assist statisticians and programmers in
-using R functions and methods to oversee the outputs often produced by
-SAS from vendors in clinical trials. The data sets would be ADaM format
-preferably, but they do not have to follow the CDISC standard.
+The goal of `stabiot` is to assist statisticians and statistical
+programmers in using R functions and methods to oversee the outputs
+produced by SAS from outsourcing in clinical trials. The data sets would
+be ADaM format preferably, but they do not have to strictly follow the
+CDISC standards.
 
-In order to ensure the quality and accuracy of the results, I prefer to
-wrap mature R package rather than rebuild the statistical methods. For
-now the completed sections are listed as shown below.
+To guarantee accurate results, I prefer to wrap mature R package rather
+than rebuild statistical methods. For present, the completed sections
+are listed below.
 
 - Simulation of sample size determination by Bayesian.
 - Summarize Least-squares Means from models, such as ANCOVA and MMRM.
-- Computing response rate, odds ratio with or without stratification,
-  and corresponding confidence interval.
+- Compute response rate, odds ratio with or without stratification, and
+  corresponding confidence interval.
+- Derive best overall response (confirmed or not confirmed BOR) per
+  RECIST 1.1
 
 ## Installation
 

inst/WORDLIST

@@ -1,5 +1,10 @@
+ADRS
+ADT
 ADaM
 ANCOVA
+AVAL
+AVALC
+BOR
 BinomCI
 BinomDiffCI
 CDISC
@@ -12,6 +17,7 @@ MMRM
 OddsRatio
 PN
 Pre
+RECIST
 TRT
 clogit
 magrittr

tests/spelling.R

@@ -1,6 +1,3 @@
-if (requireNamespace("spelling", quietly = TRUE)) {
-  spelling::spell_check_test(
-    vignettes = TRUE, error = FALSE,
-    skip_on_cran = TRUE
-  )
-}
+if(requireNamespace('spelling', quietly = TRUE))
+  spelling::spell_check_test(vignettes = TRUE, error = FALSE,
+                             skip_on_cran = TRUE)