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+The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read <http://www.gnu.org/philosophy/why-not-lgpl.html>.
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diff --git a/docs/articles/Single_analysis_workflow.html b/docs/articles/Single_analysis_workflow.html
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+Analyse2_with_ADMI
+ + + +Analyse2_with_ADMI.RmdAnalysis sketch - this version uses analyse and pool functions in +RBMI based on the example in the vignette https://insightsengineering.github.io/rbmi/main/articles/advanced.html
+The analysis is as follows: Rubin’s Rules after standardization: +Following multiple imputation, we would fit 1000 logistic regression +models and for each, obtain the marginal estimates using beeca. We would +then pool these 1000 beeca outputs using Rubin’s Rules.
+
+# Load libraries
+library(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(tidyr)
+library(readr)
+library(purrr)
+library(rbmi)
+#>
+#> Attaching package: 'rbmi'
+#> The following object is masked from 'package:tidyr':
+#>
+#> expand
+library(beeca)
+
+library(rbmiUtils)
+
+data("ADEFF")
+
+## Global constants
+N_IMPUTATIONS <- 100 ## set to 1,000 for final analysis
+
+# Prepare the data
+ADEFF$TRT <- factor(ADEFF$TRT01P, levels = c("Placebo", "Drug A"))
+ADEFF$USUBJID <- factor(ADEFF$USUBJID)
+ADEFF$AVISIT <- factor(ADEFF$AVISIT)Define function to compare last visit using a responder analysis
+
+## function to compare last visit using a responder analysis
+gcomp_responder <- function(data, ...) {
+
+ ## fit the GLM model
+ model <- glm(CRIT1FLN ~ TRT + BASE + STRATA + REGION, data = data, family = binomial)
+
+ ## Calculate the marginal treatment effect estimate and associated variance
+ ## for a difference contrast using the Ge et al. method
+ ## with robust HC0 sandwich variance
+ marginal_fit <- get_marginal_effect(model,
+ trt = "TRT",
+ method = "Ge",
+ type = "HC0",
+ contrast = "diff",
+ reference = "Placebo")
+
+ ## Extract results
+ marginal_results <- marginal_fit$marginal_results
+ diff_est <- marginal_results[marginal_results$STAT == "diff", "STATVAL"][[1]]
+ diff_se <- marginal_results[marginal_results$STAT == "diff_se", "STATVAL"][[1]]
+
+ # Return a named list with the estimate and standard error
+ res <- list(
+ trt = list(
+ est = diff_est,
+ se = diff_se,
+ df = NA
+ )
+ )
+ return(res)
+}
+
+
+STEP 1: Impute missing data +
+
+# Define key variables for rbmi
+vars <- rbmi::set_vars(
+ subjid = "USUBJID",
+ visit = "AVISIT",
+ group = "TRT",
+ outcome = "CHG",
+ covariates = c("BASE", "STRATA", "REGION")
+)
+
+# Define which imputation method to use (here: Bayesian multiple imputation with 150 imputed datsets)
+method <- rbmi::method_bayes(
+ n_samples = N_IMPUTATIONS,
+ burn_in = 200,
+ burn_between = 5,
+ seed = 675442751
+)
+
+# Create a dataset with the necessary columns
+dat <- ADEFF |>
+ select(USUBJID, STRATA, REGION, REGIONC, TRT, BASE, CHG, AVISIT)
+
+# Fit the imputation model
+draws_obj <- rbmi::draws(
+ data = dat,
+ vars = vars,
+ method = method,
+ quiet = TRUE
+)
+
+# Impute the datasets
+impute_obj <- rbmi::impute(
+ draws_obj,
+ references = c("Placebo" = "Placebo", "Drug A" = "Placebo")
+)
+
+
+## get the imputed datq as a data frame
+ADMI <- get_imputed_data(impute_obj)Transform the imputed data to include additional derived +variables.
+For example, add responder criteria based on the outcome: We convert +the continuous version of the endpoint into binary version of the +endpoint (e.g. if the endpoint>3, then it is a responder; if it is +<=3, then it is a non-responder).
+ +
+
+ STEP 2: Analyze each imputed dataset +
+
+
+
+ANCOVA analysis +
+
+ana_obj_ancova <- analyse_mi_data(data = ADMI,
+ vars = vars,
+ method = method,
+ fun = ancova,
+ delta = NULL)
+
+
+STEP 3: Pool analyzes and apply Rubin’s Rules +
+
+pool_obj_ancova <- pool(ana_obj_ancova)
+
+
+STEP 4: Report analysis +
+
+pool_obj_ancova
+#>
+#> Pool Object
+#> -----------
+#> Number of Results Combined: 100
+#> Method: rubin
+#> Confidence Level: 0.95
+#> Alternative: two.sided
+#>
+#> Results:
+#>
+#> ========================================================
+#> parameter est se lci uci pval
+#> --------------------------------------------------------
+#> trt_Week 24 -2.169 0.182 -2.528 -1.811 <0.001
+#> lsm_ref_Week 24 0.075 0.131 -0.181 0.332 0.564
+#> lsm_alt_Week 24 -2.094 0.126 -2.342 -1.847 <0.001
+#> trt_Week 48 -3.81 0.257 -4.315 -3.305 <0.001
+#> lsm_ref_Week 48 0.041 0.186 -0.324 0.406 0.827
+#> lsm_alt_Week 48 -3.769 0.176 -4.115 -3.423 <0.001
+#> --------------------------------------------------------
+
+
+Responder analysis +
+Note the vars may change from imputation to analysis
+Here we focus on the new derived variable CRIT1, CRIT1FL, +CRIT1FLN
+
+
+## note the vars may change from imputation to analysis
+vars <- set_vars(
+ subjid = "USUBJID",
+ visit = "AVISIT",
+ group = "TRT",
+ outcome = "CRIT1FLN",
+ covariates = c("BASE", "STRATA", "REGION")
+)
+
+
+Analyse the imputed datasets +
+Need to do the data transform in the fun
+
+ana_obj_prop2 <- analyse_mi_data(data = ADMI,
+ vars = vars,
+ method = method,
+ fun = gcomp_responder,
+ delta = NULL)
+
+
+STEP 3: Pool analyzes and apply Rubin’s Rules +
+
+pool_obj_prop <- pool(ana_obj_prop2)
+
+
+STEP 4: Report analysis +
+
+pool_obj_prop
+#>
+#> Pool Object
+#> -----------
+#> Number of Results Combined: 100
+#> Method: rubin
+#> Confidence Level: 0.95
+#> Alternative: two.sided
+#>
+#> Results:
+#>
+#> ==================================================
+#> parameter est se lci uci pval
+#> --------------------------------------------------
+#> trt -0.063 0.012 -0.087 -0.039 <0.001
+#> --------------------------------------------------
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/articles/delta_analyse_mi_data.html b/docs/articles/delta_analyse_mi_data.html
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+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Single_analysis_workflow
+ + + +Single_analysis_workflow.Rmd
+library(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(rbmi)
+library(rbmiUtils)
+library(beeca)This is an example of the {rbmi} only workflow. The analysis is as +follows: Rubin’s Rules after standardization: Following multiple +imputation, we would fit 100 logistic regression models and for each, +obtain the marginal estimates using beeca. We would then pool these 100 +beeca outputs using Rubin’s Rules.
+This example exists to compare with the workflow which saves an ADMI +data set, then uses {rbmiUtils} to complete the intended analysis.
+
+data("ADEFF")
+
+## Global constants
+N_IMPUTATIONS <- 100
+
+# Prepare the data
+ADEFF$TRT <- factor(ADEFF$TRT01P, levels = c("Placebo", "Drug A"))
+ADEFF$USUBJID <- factor(ADEFF$USUBJID)
+ADEFF$AVISIT <- factor(ADEFF$AVISIT)Define function to compare last visit using a responder analysis
+
+## function to compare last visit using a responder analysis
+gcomp_responder <- function(data, ...) {
+
+ data <- data |>
+ dplyr::mutate(
+ CRIT1FLN = ifelse(CHG > 3, 1, 0),
+ CRIT1FL = ifelse(CRIT1FLN == 1, "Y", "N"),
+ CRIT = "CHG > 3"
+ )
+
+ ## fit the GLM model
+ model <- glm(CRIT1FLN ~ TRT + BASE + STRATA + REGION, data = data, family = binomial)
+
+ ## Calculate the marginal treatment effect estimate and associated variance
+ ## for a difference contrast using the Ge et al. method
+ ## with robust HC0 sandwich variance
+ marginal_fit <- get_marginal_effect(model,
+ trt = "TRT",
+ method = "Ge",
+ type = "HC0",
+ contrast = "diff",
+ reference = "Placebo")
+
+ ## Extract results
+ marginal_results <- marginal_fit$marginal_results
+ diff_est <- marginal_results[marginal_results$STAT == "diff", "STATVAL"][[1]]
+ diff_se <- marginal_results[marginal_results$STAT == "diff_se", "STATVAL"][[1]]
+
+ # Return a named list with the estimate and standard error
+ res <- list(
+ trt = list(
+ est = diff_est,
+ se = diff_se,
+ df = NA
+ )
+ )
+ return(res)
+}
+
+
+STEP 1: Impute missing data +
+
+# Define key variables for rbmi
+vars <- rbmi::set_vars(
+ subjid = "USUBJID",
+ visit = "AVISIT",
+ group = "TRT",
+ outcome = "CHG",
+ covariates = c("BASE", "STRATA", "REGION")
+)
+
+# Define which imputation method to use (here: Bayesian multiple imputation with 150 imputed datsets)
+method <- rbmi::method_bayes(
+ n_samples = N_IMPUTATIONS,
+ burn_in = 200,
+ burn_between = 5,
+ seed = 675442751
+)
+
+# Create a dataset with the necessary columns
+dat <- ADEFF |>
+ select(USUBJID, STRATA, REGION, REGIONC, TRT, BASE, CHG, AVISIT)
+
+# Fit the imputation model
+draws_obj <- rbmi::draws(
+ data = dat,
+ vars = vars,
+ method = method,
+ quiet = TRUE
+)
+
+# Impute the datasets
+impute_obj <- rbmi::impute(
+ draws_obj,
+ references = c("Placebo" = "Placebo", "Drug A" = "Placebo")
+)
+
+ STEP 2: Analyze each imputed dataset +
+
+
+
+ANCOVA analysis +
+
+ana_obj_prop <- analyse(impute_obj,
+ vars = vars,
+ fun = ancova,
+ delta = NULL)
+
+
+STEP 3: Pool analyzes and apply Rubin’s Rules +
+
+pool_obj_prop <- pool(ana_obj_prop)
+
+
+STEP 4: Report analysis +
+
+pool_obj_prop
+#>
+#> Pool Object
+#> -----------
+#> Number of Results Combined: 100
+#> Method: rubin
+#> Confidence Level: 0.95
+#> Alternative: two.sided
+#>
+#> Results:
+#>
+#> ========================================================
+#> parameter est se lci uci pval
+#> --------------------------------------------------------
+#> trt_Week 24 -2.169 0.182 -2.528 -1.811 <0.001
+#> lsm_ref_Week 24 0.075 0.131 -0.181 0.332 0.564
+#> lsm_alt_Week 24 -2.094 0.126 -2.342 -1.847 <0.001
+#> trt_Week 48 -3.81 0.257 -4.315 -3.305 <0.001
+#> lsm_ref_Week 48 0.041 0.186 -0.324 0.406 0.827
+#> lsm_alt_Week 48 -3.769 0.176 -4.115 -3.423 <0.001
+#> --------------------------------------------------------
+
+
+Responder analysis +
+
+ana_obj_prop <- analyse(impute_obj,
+ vars = vars,
+ method = method,
+ fun = gcomp_responder,
+ delta = NULL)
+
+
+STEP 3: Pool analyzes and apply Rubin’s Rules +
+
+pool_obj_prop <- pool(ana_obj_prop)
+
+
+STEP 4: Report analysis +
+
+pool_obj_prop
+#>
+#> Pool Object
+#> -----------
+#> Number of Results Combined: 100
+#> Method: rubin
+#> Confidence Level: 0.95
+#> Alternative: two.sided
+#>
+#> Results:
+#>
+#> ==================================================
+#> parameter est se lci uci pval
+#> --------------------------------------------------
+#> trt -0.063 0.012 -0.087 -0.039 <0.001
+#> --------------------------------------------------
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/articles/delta_analyse_mi_data_files/figure-html/unnamed-chunk-6-1.png b/docs/articles/delta_analyse_mi_data_files/figure-html/unnamed-chunk-6-1.png
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+
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+
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+
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+
+WIP: Delta Analysis of Imputed Data
+ + + +delta_analyse_mi_data.Rmd
+
+Introduction +
+This vignette demonstrates the process of analyzing imputed data
+using the rbmi package, specifically focusing on applying a
+delta adjustment to missing values and running a tipping point
+analysis.
+
+
+Prerequisites +
+
+library(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(rbmi)
+library(rbmiUtils)
+library(ggplot2)
+library(purrr)
+
+set.seed(122)
+data("ADEFF")
+
+# Constants
+N_IMPUTATIONS <- 100 # Set to 1000 for final analysis
+
+# Prepare the data
+ADEFF <- ADEFF %>%
+ mutate(
+ TRT = factor(TRT01P, levels = c("Placebo", "Drug A")),
+ USUBJID = factor(USUBJID),
+ AVISIT = factor(AVISIT)
+ )
+
+
+Step 1: Impute Missing Data +
+In this step, we set up the necessary variables and impute missing +data using Bayesian multiple imputation.
+
+# Define key variables for rbmi
+vars <- rbmi::set_vars(
+ subjid = "USUBJID",
+ visit = "AVISIT",
+ group = "TRT",
+ outcome = "CHG",
+ covariates = c("BASE", "STRATA", "REGION")
+)
+
+# Define the imputation method (Bayesian)
+method <- rbmi::method_bayes(
+ n_samples = N_IMPUTATIONS,
+ burn_in = 200,
+ burn_between = 5,
+ seed = 675442751
+)
+
+# Subset relevant columns
+dat <- ADEFF %>%
+ select(USUBJID, STRATA, REGION, REGIONC, TRT, BASE, CHG, AVISIT)
+
+# Fit the imputation model and perform imputation
+draws_obj <- rbmi::draws(data = dat, vars = vars, method = method, quiet = TRUE)
+impute_obj <- rbmi::impute(draws_obj, references = c("Placebo" = "Placebo", "Drug A" = "Placebo"))
+
+# Get the imputed data as a data frame
+ADMI <- get_imputed_data(impute_obj)
+
+
+Step 2: Delta Adjustment +
+We now apply a delta adjustment to imputed outcomes, setting a delta +value of 0.5 for all missing outcomes.
+
+# Create delta adjustment template
+dat_delta <- delta_template(imputations = impute_obj) %>%
+ mutate(delta = is_missing * 0.5)
+
+glimpse(dat_delta)
+#> Rows: 1,000
+#> Columns: 8
+#> $ USUBJID <fct> ID001, ID001, ID002, ID002, ID003, ID003, ID004, ID004, ID…
+#> $ AVISIT <fct> Week 24, Week 48, Week 24, Week 48, Week 24, Week 48, Week…
+#> $ TRT <fct> Drug A, Drug A, Drug A, Drug A, Drug A, Drug A, Placebo, P…
+#> $ is_mar <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE…
+#> $ is_missing <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FA…
+#> $ is_post_ice <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FA…
+#> $ strategy <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
+#> $ delta <dbl> 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0…
+
+
+Step 3: Analyze Each Imputed Dataset +
+Here, we perform an ANCOVA analysis on the imputed datasets after +applying the delta adjustment.
+
+ana_obj_ancova <- analyse_mi_data(
+ data = ADMI,
+ vars = vars,
+ method = method,
+ fun = ancova,
+ delta = dat_delta
+)
+
+
+Step 4: Pool the Results +
+We pool the results of the ANCOVA analyses across all imputed +datasets using Rubin’s Rules.
+
+pool_obj_ancova <- pool(ana_obj_ancova)
+pool_obj_ancova
+#>
+#> Pool Object
+#> -----------
+#> Number of Results Combined: 100
+#> Method: rubin
+#> Confidence Level: 0.95
+#> Alternative: two.sided
+#>
+#> Results:
+#>
+#> ========================================================
+#> parameter est se lci uci pval
+#> --------------------------------------------------------
+#> trt_Week 24 -2.162 0.182 -2.52 -1.803 <0.001
+#> lsm_ref_Week 24 0.088 0.131 -0.169 0.345 0.499
+#> lsm_alt_Week 24 -2.073 0.126 -2.321 -1.826 <0.001
+#> trt_Week 48 -3.821 0.257 -4.325 -3.316 <0.001
+#> lsm_ref_Week 48 0.073 0.185 -0.291 0.438 0.693
+#> lsm_alt_Week 48 -3.747 0.176 -4.093 -3.402 <0.001
+#> --------------------------------------------------------
+
+
+ Tipping Point Analysis +
+Next, we conduct a tipping point analysis to assess the sensitivity +of the results to various delta offsets for the control and intervention +groups.
+WIP: the values do not make scientific sense and are to illustrate a +difference.
+
+perform_tipp_analysis <- function(delta_control, delta_intervention) {
+ delta_df <- delta_df_init %>%
+ mutate(
+ delta_ctl = (TRT == "Placebo") * is_missing * delta_control,
+ delta_int = (TRT == "Drug A") * is_missing * delta_intervention,
+ delta = delta_ctl + delta_int
+ )
+
+ ana_obj_delta <- analyse_mi_data(
+ data = ADMI %>% filter(AVISIT == "Week 48"),
+ vars = vars,
+ method = method,
+ fun = ancova,
+ delta = delta_df
+ )
+
+ pool_delta <- as.data.frame(pool(ana_obj_delta)) %>%
+ filter(parameter == "trt_Week 48")
+
+ list(
+ trt_effect_6 = pool_delta[["est"]],
+ pval_6 = pool_delta[["pval"]]
+ )
+}
+
+# Initial delta template
+delta_df_init <- delta_template(impute_obj) %>%
+ filter(AVISIT == "Week 48")
+
+# # Define delta ranges for control and intervention groups
+# tipp_frame_grid <- expand.grid(
+# delta_control = seq(-10, 10, by = 1),
+# delta_intervention = seq(-10, 10, by = 1)
+# ) %>%
+# as_tibble()
+#
+# # Apply tipping point analysis across the grid
+# tipp_frame <- tipp_frame_grid %>%
+# mutate(
+# results_list = map2(delta_control, delta_intervention, perform_tipp_analysis),
+# trt_effect_6 = map_dbl(results_list, "trt_effect_6"),
+# pval_6 = map_dbl(results_list, "pval_6")
+# ) %>%
+# select(-results_list) %>%
+# mutate(
+# pval = cut(
+# pval_6,
+# c(0, 0.001, 0.01, 0.05, 0.2, 1),
+# right = FALSE,
+# labels = c("<0.001", "0.001 - <0.01", "0.01- <0.05", "0.05 - <0.20", ">= 0.20")
+# )
+# )
+#
+# # Display delta values leading to non-significant results
+# tipp_frame %>%
+# filter(pval_6 >= 0.05)
+#
+# # Plot the tipping point results
+# ggplot(tipp_frame, aes(delta_control, delta_intervention, fill = pval)) +
+# geom_raster() +
+# scale_fill_manual(values = c("darkgreen", "lightgreen", "lightyellow", "orange", "red"))
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/authors.html b/docs/authors.html
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+
+
+
+
+
+
+
+
+
+
+ Articles
+
+
+ All vignettes
+ + +`, then ``, etc.) that has more than one element. Defaults to 1 (for ``).
+ getTopLevel: function($scope) {
+ for (var i = 1; i <= 6; i++) {
+ var $headings = this.findOrFilter($scope, 'h' + i);
+ if ($headings.length > 1) {
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+ }
+ }
+
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+ },
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+
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+ } // else use the current $context
+
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+
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+ parseOps: function(arg) {
+ var opts;
+ if (arg.jquery) {
+ opts = {
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+ };
+ } else {
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+ }
+ opts.$scope = opts.$scope || $(document.body);
+ return opts;
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+
+ // accepts a jQuery object, or an options object
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+ opts = this.helpers.parseOps(opts);
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+ // ensure that the data attribute is in place for styling
+ opts.$nav.attr('data-toggle', 'toc');
+
+ var $topContext = this.helpers.createChildNavList(opts.$nav);
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+ var $headings = this.helpers.getHeadings(opts.$scope, topLevel);
+ this.helpers.populateNav($topContext, topLevel, $headings);
+ }
+ };
+
+ $(function() {
+ $('nav[data-toggle="toc"]').each(function(i, el) {
+ var $nav = $(el);
+ Toc.init($nav);
+ });
+ });
+})();
diff --git a/docs/docsearch.css b/docs/docsearch.css
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+++ b/docs/docsearch.css
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+/* Docsearch -------------------------------------------------------------- */
+/*
+ Source: https://github.com/algolia/docsearch/
+ License: MIT
+*/
+
+.algolia-autocomplete {
+ display: block;
+ -webkit-box-flex: 1;
+ -ms-flex: 1;
+ flex: 1
+}
+
+.algolia-autocomplete .ds-dropdown-menu {
+ width: 100%;
+ min-width: none;
+ max-width: none;
+ padding: .75rem 0;
+ background-color: #fff;
+ background-clip: padding-box;
+ border: 1px solid rgba(0, 0, 0, .1);
+ box-shadow: 0 .5rem 1rem rgba(0, 0, 0, .175);
+}
+
+@media (min-width:768px) {
+ .algolia-autocomplete .ds-dropdown-menu {
+ width: 175%
+ }
+}
+
+.algolia-autocomplete .ds-dropdown-menu::before {
+ display: none
+}
+
+.algolia-autocomplete .ds-dropdown-menu [class^=ds-dataset-] {
+ padding: 0;
+ background-color: rgb(255,255,255);
+ border: 0;
+ max-height: 80vh;
+}
+
+.algolia-autocomplete .ds-dropdown-menu .ds-suggestions {
+ margin-top: 0
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion {
+ padding: 0;
+ overflow: visible
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--category-header {
+ padding: .125rem 1rem;
+ margin-top: 0;
+ font-size: 1.3em;
+ font-weight: 500;
+ color: #00008B;
+ border-bottom: 0
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--wrapper {
+ float: none;
+ padding-top: 0
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--subcategory-column {
+ float: none;
+ width: auto;
+ padding: 0;
+ text-align: left
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--content {
+ float: none;
+ width: auto;
+ padding: 0
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--content::before {
+ display: none
+}
+
+.algolia-autocomplete .ds-suggestion:not(:first-child) .algolia-docsearch-suggestion--category-header {
+ padding-top: .75rem;
+ margin-top: .75rem;
+ border-top: 1px solid rgba(0, 0, 0, .1)
+}
+
+.algolia-autocomplete .ds-suggestion .algolia-docsearch-suggestion--subcategory-column {
+ display: block;
+ padding: .1rem 1rem;
+ margin-bottom: 0.1;
+ font-size: 1.0em;
+ font-weight: 400
+ /* display: none */
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--title {
+ display: block;
+ padding: .25rem 1rem;
+ margin-bottom: 0;
+ font-size: 0.9em;
+ font-weight: 400
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--text {
+ padding: 0 1rem .5rem;
+ margin-top: -.25rem;
+ font-size: 0.8em;
+ font-weight: 400;
+ line-height: 1.25
+}
+
+.algolia-autocomplete .algolia-docsearch-footer {
+ width: 110px;
+ height: 20px;
+ z-index: 3;
+ margin-top: 10.66667px;
+ float: right;
+ font-size: 0;
+ line-height: 0;
+}
+
+.algolia-autocomplete .algolia-docsearch-footer--logo {
+ background-image: url("data:image/svg+xml;utf8,");
+ background-repeat: no-repeat;
+ background-position: 50%;
+ background-size: 100%;
+ overflow: hidden;
+ text-indent: -9000px;
+ width: 100%;
+ height: 100%;
+ display: block;
+ transform: translate(-8px);
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--highlight {
+ color: #FF8C00;
+ background: rgba(232, 189, 54, 0.1)
+}
+
+
+.algolia-autocomplete .algolia-docsearch-suggestion--text .algolia-docsearch-suggestion--highlight {
+ box-shadow: inset 0 -2px 0 0 rgba(105, 105, 105, .5)
+}
+
+.algolia-autocomplete .ds-suggestion.ds-cursor .algolia-docsearch-suggestion--content {
+ background-color: rgba(192, 192, 192, .15)
+}
diff --git a/docs/docsearch.js b/docs/docsearch.js
new file mode 100644
index 0000000..b35504c
--- /dev/null
+++ b/docs/docsearch.js
@@ -0,0 +1,85 @@
+$(function() {
+
+ // register a handler to move the focus to the search bar
+ // upon pressing shift + "/" (i.e. "?")
+ $(document).on('keydown', function(e) {
+ if (e.shiftKey && e.keyCode == 191) {
+ e.preventDefault();
+ $("#search-input").focus();
+ }
+ });
+
+ $(document).ready(function() {
+ // do keyword highlighting
+ /* modified from https://jsfiddle.net/julmot/bL6bb5oo/ */
+ var mark = function() {
+
+ var referrer = document.URL ;
+ var paramKey = "q" ;
+
+ if (referrer.indexOf("?") !== -1) {
+ var qs = referrer.substr(referrer.indexOf('?') + 1);
+ var qs_noanchor = qs.split('#')[0];
+ var qsa = qs_noanchor.split('&');
+ var keyword = "";
+
+ for (var i = 0; i < qsa.length; i++) {
+ var currentParam = qsa[i].split('=');
+
+ if (currentParam.length !== 2) {
+ continue;
+ }
+
+ if (currentParam[0] == paramKey) {
+ keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20"));
+ }
+ }
+
+ if (keyword !== "") {
+ $(".contents").unmark({
+ done: function() {
+ $(".contents").mark(keyword);
+ }
+ });
+ }
+ }
+ };
+
+ mark();
+ });
+});
+
+/* Search term highlighting ------------------------------*/
+
+function matchedWords(hit) {
+ var words = [];
+
+ var hierarchy = hit._highlightResult.hierarchy;
+ // loop to fetch from lvl0, lvl1, etc.
+ for (var idx in hierarchy) {
+ words = words.concat(hierarchy[idx].matchedWords);
+ }
+
+ var content = hit._highlightResult.content;
+ if (content) {
+ words = words.concat(content.matchedWords);
+ }
+
+ // return unique words
+ var words_uniq = [...new Set(words)];
+ return words_uniq;
+}
+
+function updateHitURL(hit) {
+
+ var words = matchedWords(hit);
+ var url = "";
+
+ if (hit.anchor) {
+ url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor;
+ } else {
+ url = hit.url + '?q=' + escape(words.join(" "));
+ }
+
+ return url;
+}
diff --git a/docs/index.html b/docs/index.html
new file mode 100644
index 0000000..2f111e2
--- /dev/null
+++ b/docs/index.html
@@ -0,0 +1,224 @@
+
+
+
+
+
+
+
+Utility functions to work with and extend the rbmi package • rbmiUtils
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+The goal of rbmiUtils is to provide additional functionality to {rbmi} for clinical trial analysis and reporting. This package is currently under development and is not yet available on CRAN.
+
+Local installation (for beta-testing only)
+
+You can install the latest version of rbmiUtils from GitHub:
+
+if (!require("remotes")) {
+ install.packages("remotes")
+}
+remotes::install_github("openpharma/rbmiUtils")
+After the official release, the package installation will differ.
+
+
+Example
+
+This is a basic example which shows you how to solve a common problem:
+
+# Example usage:
+library(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(rbmi)
+library(rbmiUtils)
+
+data("ADMI")
+N_IMPUTATIONS <- 100
+BURN_IN <- 200
+BURN_BETWEEN <- 5
+
+# Convert key columns to factors
+ADMI$TRT <- factor(ADMI$TRT, levels = c("Placebo", "Drug A"))
+ADMI$USUBJID <- factor(ADMI$USUBJID)
+ADMI$AVISIT <- factor(ADMI$AVISIT)
+
+# Define key variables for ANCOVA analysis
+ vars <- set_vars(
+ subjid = "USUBJID",
+ visit = "AVISIT",
+ group = "TRT",
+ outcome = "CHG",
+ covariates = c("BASE", "STRATA", "REGION") # Covariates for adjustment
+ )
+
+# Specify the imputation method (Bayesian) - need for pool step
+method <- rbmi::method_bayes(
+ n_samples = N_IMPUTATIONS,
+ burn_in = BURN_IN,
+ burn_between = BURN_BETWEEN
+)
+
+# Perform ANCOVA Analysis on Each Imputed Dataset
+ana_obj_ancova <- analyse_mi_data(
+ data = ADMI,
+ vars = vars,
+ method = method,
+ fun = ancova, # Apply ANCOVA
+ delta = NULL # No sensitivity analysis adjustment
+)
+
+pool_obj_ancova <- pool(ana_obj_ancova)
+tidy_df <- tidy_pool_obj(pool_obj_ancova)
+
+# Print tidy data frames
+print(tidy_df)
+#> # A tibble: 6 × 10
+#> parameter description visit parameter_type lsm_type est se lci
+#> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
+#> 1 trt_Week 24 Treatment … Week… trt <NA> -2.17 0.182 -2.53
+#> 2 lsm_ref_Week 24 Least Squa… Week… lsm ref 0.0782 0.131 -0.179
+#> 3 lsm_alt_Week 24 Least Squa… Week… lsm alt -2.09 0.126 -2.34
+#> 4 trt_Week 48 Treatment … Week… trt <NA> -3.81 0.256 -4.31
+#> 5 lsm_ref_Week 48 Least Squa… Week… lsm ref 0.0481 0.185 -0.316
+#> 6 lsm_alt_Week 48 Least Squa… Week… lsm alt -3.76 0.176 -4.11
+#> # ℹ 2 more variables: uci <dbl>, pval <dbl>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/link.svg b/docs/link.svg
new file mode 100644
index 0000000..88ad827
--- /dev/null
+++ b/docs/link.svg
@@ -0,0 +1,12 @@
+
+
+
diff --git a/docs/news/index.html b/docs/news/index.html
new file mode 100644
index 0000000..f9aee2e
--- /dev/null
+++ b/docs/news/index.html
@@ -0,0 +1,93 @@
+
+Changelog • rbmiUtils
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/pkgdown.css b/docs/pkgdown.css
new file mode 100644
index 0000000..80ea5b8
--- /dev/null
+++ b/docs/pkgdown.css
@@ -0,0 +1,384 @@
+/* Sticky footer */
+
+/**
+ * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/
+ * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css
+ *
+ * .Site -> body > .container
+ * .Site-content -> body > .container .row
+ * .footer -> footer
+ *
+ * Key idea seems to be to ensure that .container and __all its parents__
+ * have height set to 100%
+ *
+ */
+
+html, body {
+ height: 100%;
+}
+
+body {
+ position: relative;
+}
+
+body > .container {
+ display: flex;
+ height: 100%;
+ flex-direction: column;
+}
+
+body > .container .row {
+ flex: 1 0 auto;
+}
+
+footer {
+ margin-top: 45px;
+ padding: 35px 0 36px;
+ border-top: 1px solid #e5e5e5;
+ color: #666;
+ display: flex;
+ flex-shrink: 0;
+}
+footer p {
+ margin-bottom: 0;
+}
+footer div {
+ flex: 1;
+}
+footer .pkgdown {
+ text-align: right;
+}
+footer p {
+ margin-bottom: 0;
+}
+
+img.icon {
+ float: right;
+}
+
+/* Ensure in-page images don't run outside their container */
+.contents img {
+ max-width: 100%;
+ height: auto;
+}
+
+/* Fix bug in bootstrap (only seen in firefox) */
+summary {
+ display: list-item;
+}
+
+/* Typographic tweaking ---------------------------------*/
+
+.contents .page-header {
+ margin-top: calc(-60px + 1em);
+}
+
+dd {
+ margin-left: 3em;
+}
+
+/* Section anchors ---------------------------------*/
+
+a.anchor {
+ display: none;
+ margin-left: 5px;
+ width: 20px;
+ height: 20px;
+
+ background-image: url(./link.svg);
+ background-repeat: no-repeat;
+ background-size: 20px 20px;
+ background-position: center center;
+}
+
+h1:hover .anchor,
+h2:hover .anchor,
+h3:hover .anchor,
+h4:hover .anchor,
+h5:hover .anchor,
+h6:hover .anchor {
+ display: inline-block;
+}
+
+/* Fixes for fixed navbar --------------------------*/
+
+.contents h1, .contents h2, .contents h3, .contents h4 {
+ padding-top: 60px;
+ margin-top: -40px;
+}
+
+/* Navbar submenu --------------------------*/
+
+.dropdown-submenu {
+ position: relative;
+}
+
+.dropdown-submenu>.dropdown-menu {
+ top: 0;
+ left: 100%;
+ margin-top: -6px;
+ margin-left: -1px;
+ border-radius: 0 6px 6px 6px;
+}
+
+.dropdown-submenu:hover>.dropdown-menu {
+ display: block;
+}
+
+.dropdown-submenu>a:after {
+ display: block;
+ content: " ";
+ float: right;
+ width: 0;
+ height: 0;
+ border-color: transparent;
+ border-style: solid;
+ border-width: 5px 0 5px 5px;
+ border-left-color: #cccccc;
+ margin-top: 5px;
+ margin-right: -10px;
+}
+
+.dropdown-submenu:hover>a:after {
+ border-left-color: #ffffff;
+}
+
+.dropdown-submenu.pull-left {
+ float: none;
+}
+
+.dropdown-submenu.pull-left>.dropdown-menu {
+ left: -100%;
+ margin-left: 10px;
+ border-radius: 6px 0 6px 6px;
+}
+
+/* Sidebar --------------------------*/
+
+#pkgdown-sidebar {
+ margin-top: 30px;
+ position: -webkit-sticky;
+ position: sticky;
+ top: 70px;
+}
+
+#pkgdown-sidebar h2 {
+ font-size: 1.5em;
+ margin-top: 1em;
+}
+
+#pkgdown-sidebar h2:first-child {
+ margin-top: 0;
+}
+
+#pkgdown-sidebar .list-unstyled li {
+ margin-bottom: 0.5em;
+}
+
+/* bootstrap-toc tweaks ------------------------------------------------------*/
+
+/* All levels of nav */
+
+nav[data-toggle='toc'] .nav > li > a {
+ padding: 4px 20px 4px 6px;
+ font-size: 1.5rem;
+ font-weight: 400;
+ color: inherit;
+}
+
+nav[data-toggle='toc'] .nav > li > a:hover,
+nav[data-toggle='toc'] .nav > li > a:focus {
+ padding-left: 5px;
+ color: inherit;
+ border-left: 1px solid #878787;
+}
+
+nav[data-toggle='toc'] .nav > .active > a,
+nav[data-toggle='toc'] .nav > .active:hover > a,
+nav[data-toggle='toc'] .nav > .active:focus > a {
+ padding-left: 5px;
+ font-size: 1.5rem;
+ font-weight: 400;
+ color: inherit;
+ border-left: 2px solid #878787;
+}
+
+/* Nav: second level (shown on .active) */
+
+nav[data-toggle='toc'] .nav .nav {
+ display: none; /* Hide by default, but at >768px, show it */
+ padding-bottom: 10px;
+}
+
+nav[data-toggle='toc'] .nav .nav > li > a {
+ padding-left: 16px;
+ font-size: 1.35rem;
+}
+
+nav[data-toggle='toc'] .nav .nav > li > a:hover,
+nav[data-toggle='toc'] .nav .nav > li > a:focus {
+ padding-left: 15px;
+}
+
+nav[data-toggle='toc'] .nav .nav > .active > a,
+nav[data-toggle='toc'] .nav .nav > .active:hover > a,
+nav[data-toggle='toc'] .nav .nav > .active:focus > a {
+ padding-left: 15px;
+ font-weight: 500;
+ font-size: 1.35rem;
+}
+
+/* orcid ------------------------------------------------------------------- */
+
+.orcid {
+ font-size: 16px;
+ color: #A6CE39;
+ /* margins are required by official ORCID trademark and display guidelines */
+ margin-left:4px;
+ margin-right:4px;
+ vertical-align: middle;
+}
+
+/* Reference index & topics ----------------------------------------------- */
+
+.ref-index th {font-weight: normal;}
+
+.ref-index td {vertical-align: top; min-width: 100px}
+.ref-index .icon {width: 40px;}
+.ref-index .alias {width: 40%;}
+.ref-index-icons .alias {width: calc(40% - 40px);}
+.ref-index .title {width: 60%;}
+
+.ref-arguments th {text-align: right; padding-right: 10px;}
+.ref-arguments th, .ref-arguments td {vertical-align: top; min-width: 100px}
+.ref-arguments .name {width: 20%;}
+.ref-arguments .desc {width: 80%;}
+
+/* Nice scrolling for wide elements --------------------------------------- */
+
+table {
+ display: block;
+ overflow: auto;
+}
+
+/* Syntax highlighting ---------------------------------------------------- */
+
+pre, code, pre code {
+ background-color: #f8f8f8;
+ color: #333;
+}
+pre, pre code {
+ white-space: pre-wrap;
+ word-break: break-all;
+ overflow-wrap: break-word;
+}
+
+pre {
+ border: 1px solid #eee;
+}
+
+pre .img, pre .r-plt {
+ margin: 5px 0;
+}
+
+pre .img img, pre .r-plt img {
+ background-color: #fff;
+}
+
+code a, pre a {
+ color: #375f84;
+}
+
+a.sourceLine:hover {
+ text-decoration: none;
+}
+
+.fl {color: #1514b5;}
+.fu {color: #000000;} /* function */
+.ch,.st {color: #036a07;} /* string */
+.kw {color: #264D66;} /* keyword */
+.co {color: #888888;} /* comment */
+
+.error {font-weight: bolder;}
+.warning {font-weight: bolder;}
+
+/* Clipboard --------------------------*/
+
+.hasCopyButton {
+ position: relative;
+}
+
+.btn-copy-ex {
+ position: absolute;
+ right: 0;
+ top: 0;
+ visibility: hidden;
+}
+
+.hasCopyButton:hover button.btn-copy-ex {
+ visibility: visible;
+}
+
+/* headroom.js ------------------------ */
+
+.headroom {
+ will-change: transform;
+ transition: transform 200ms linear;
+}
+.headroom--pinned {
+ transform: translateY(0%);
+}
+.headroom--unpinned {
+ transform: translateY(-100%);
+}
+
+/* mark.js ----------------------------*/
+
+mark {
+ background-color: rgba(255, 255, 51, 0.5);
+ border-bottom: 2px solid rgba(255, 153, 51, 0.3);
+ padding: 1px;
+}
+
+/* vertical spacing after htmlwidgets */
+.html-widget {
+ margin-bottom: 10px;
+}
+
+/* fontawesome ------------------------ */
+
+.fab {
+ font-family: "Font Awesome 5 Brands" !important;
+}
+
+/* don't display links in code chunks when printing */
+/* source: https://stackoverflow.com/a/10781533 */
+@media print {
+ code a:link:after, code a:visited:after {
+ content: "";
+ }
+}
+
+/* Section anchors ---------------------------------
+ Added in pandoc 2.11: https://github.com/jgm/pandoc-templates/commit/9904bf71
+*/
+
+div.csl-bib-body { }
+div.csl-entry {
+ clear: both;
+}
+.hanging-indent div.csl-entry {
+ margin-left:2em;
+ text-indent:-2em;
+}
+div.csl-left-margin {
+ min-width:2em;
+ float:left;
+}
+div.csl-right-inline {
+ margin-left:2em;
+ padding-left:1em;
+}
+div.csl-indent {
+ margin-left: 2em;
+}
diff --git a/docs/pkgdown.js b/docs/pkgdown.js
new file mode 100644
index 0000000..6f0eee4
--- /dev/null
+++ b/docs/pkgdown.js
@@ -0,0 +1,108 @@
+/* http://gregfranko.com/blog/jquery-best-practices/ */
+(function($) {
+ $(function() {
+
+ $('.navbar-fixed-top').headroom();
+
+ $('body').css('padding-top', $('.navbar').height() + 10);
+ $(window).resize(function(){
+ $('body').css('padding-top', $('.navbar').height() + 10);
+ });
+
+ $('[data-toggle="tooltip"]').tooltip();
+
+ var cur_path = paths(location.pathname);
+ var links = $("#navbar ul li a");
+ var max_length = -1;
+ var pos = -1;
+ for (var i = 0; i < links.length; i++) {
+ if (links[i].getAttribute("href") === "#")
+ continue;
+ // Ignore external links
+ if (links[i].host !== location.host)
+ continue;
+
+ var nav_path = paths(links[i].pathname);
+
+ var length = prefix_length(nav_path, cur_path);
+ if (length > max_length) {
+ max_length = length;
+ pos = i;
+ }
+ }
+
+ // Add class to parent
`).
+ getTopLevel: function($scope) {
+ for (var i = 1; i <= 6; i++) {
+ var $headings = this.findOrFilter($scope, 'h' + i);
+ if ($headings.length > 1) {
+ return i;
+ }
+ }
+
+ return 1;
+ },
+
+ // returns the elements for the top level, and the next below it
+ getHeadings: function($scope, topLevel) {
+ var topSelector = 'h' + topLevel;
+
+ var secondaryLevel = topLevel + 1;
+ var secondarySelector = 'h' + secondaryLevel;
+
+ return this.findOrFilter($scope, topSelector + ',' + secondarySelector);
+ },
+
+ getNavLevel: function(el) {
+ return parseInt(el.tagName.charAt(1), 10);
+ },
+
+ populateNav: function($topContext, topLevel, $headings) {
+ var $context = $topContext;
+ var $prevNav;
+
+ var helpers = this;
+ $headings.each(function(i, el) {
+ var $newNav = helpers.generateNavItem(el);
+ var navLevel = helpers.getNavLevel(el);
+
+ // determine the proper $context
+ if (navLevel === topLevel) {
+ // use top level
+ $context = $topContext;
+ } else if ($prevNav && $context === $topContext) {
+ // create a new level of the tree and switch to it
+ $context = helpers.createChildNavList($prevNav);
+ } // else use the current $context
+
+ $context.append($newNav);
+
+ $prevNav = $newNav;
+ });
+ },
+
+ parseOps: function(arg) {
+ var opts;
+ if (arg.jquery) {
+ opts = {
+ $nav: arg
+ };
+ } else {
+ opts = arg;
+ }
+ opts.$scope = opts.$scope || $(document.body);
+ return opts;
+ }
+ },
+
+ // accepts a jQuery object, or an options object
+ init: function(opts) {
+ opts = this.helpers.parseOps(opts);
+
+ // ensure that the data attribute is in place for styling
+ opts.$nav.attr('data-toggle', 'toc');
+
+ var $topContext = this.helpers.createChildNavList(opts.$nav);
+ var topLevel = this.helpers.getTopLevel(opts.$scope);
+ var $headings = this.helpers.getHeadings(opts.$scope, topLevel);
+ this.helpers.populateNav($topContext, topLevel, $headings);
+ }
+ };
+
+ $(function() {
+ $('nav[data-toggle="toc"]').each(function(i, el) {
+ var $nav = $(el);
+ Toc.init($nav);
+ });
+ });
+})();
diff --git a/docs/docsearch.css b/docs/docsearch.css
new file mode 100644
index 0000000..e5f1fe1
--- /dev/null
+++ b/docs/docsearch.css
@@ -0,0 +1,148 @@
+/* Docsearch -------------------------------------------------------------- */
+/*
+ Source: https://github.com/algolia/docsearch/
+ License: MIT
+*/
+
+.algolia-autocomplete {
+ display: block;
+ -webkit-box-flex: 1;
+ -ms-flex: 1;
+ flex: 1
+}
+
+.algolia-autocomplete .ds-dropdown-menu {
+ width: 100%;
+ min-width: none;
+ max-width: none;
+ padding: .75rem 0;
+ background-color: #fff;
+ background-clip: padding-box;
+ border: 1px solid rgba(0, 0, 0, .1);
+ box-shadow: 0 .5rem 1rem rgba(0, 0, 0, .175);
+}
+
+@media (min-width:768px) {
+ .algolia-autocomplete .ds-dropdown-menu {
+ width: 175%
+ }
+}
+
+.algolia-autocomplete .ds-dropdown-menu::before {
+ display: none
+}
+
+.algolia-autocomplete .ds-dropdown-menu [class^=ds-dataset-] {
+ padding: 0;
+ background-color: rgb(255,255,255);
+ border: 0;
+ max-height: 80vh;
+}
+
+.algolia-autocomplete .ds-dropdown-menu .ds-suggestions {
+ margin-top: 0
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion {
+ padding: 0;
+ overflow: visible
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--category-header {
+ padding: .125rem 1rem;
+ margin-top: 0;
+ font-size: 1.3em;
+ font-weight: 500;
+ color: #00008B;
+ border-bottom: 0
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--wrapper {
+ float: none;
+ padding-top: 0
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--subcategory-column {
+ float: none;
+ width: auto;
+ padding: 0;
+ text-align: left
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--content {
+ float: none;
+ width: auto;
+ padding: 0
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--content::before {
+ display: none
+}
+
+.algolia-autocomplete .ds-suggestion:not(:first-child) .algolia-docsearch-suggestion--category-header {
+ padding-top: .75rem;
+ margin-top: .75rem;
+ border-top: 1px solid rgba(0, 0, 0, .1)
+}
+
+.algolia-autocomplete .ds-suggestion .algolia-docsearch-suggestion--subcategory-column {
+ display: block;
+ padding: .1rem 1rem;
+ margin-bottom: 0.1;
+ font-size: 1.0em;
+ font-weight: 400
+ /* display: none */
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--title {
+ display: block;
+ padding: .25rem 1rem;
+ margin-bottom: 0;
+ font-size: 0.9em;
+ font-weight: 400
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--text {
+ padding: 0 1rem .5rem;
+ margin-top: -.25rem;
+ font-size: 0.8em;
+ font-weight: 400;
+ line-height: 1.25
+}
+
+.algolia-autocomplete .algolia-docsearch-footer {
+ width: 110px;
+ height: 20px;
+ z-index: 3;
+ margin-top: 10.66667px;
+ float: right;
+ font-size: 0;
+ line-height: 0;
+}
+
+.algolia-autocomplete .algolia-docsearch-footer--logo {
+ background-image: url("data:image/svg+xml;utf8,");
+ background-repeat: no-repeat;
+ background-position: 50%;
+ background-size: 100%;
+ overflow: hidden;
+ text-indent: -9000px;
+ width: 100%;
+ height: 100%;
+ display: block;
+ transform: translate(-8px);
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--highlight {
+ color: #FF8C00;
+ background: rgba(232, 189, 54, 0.1)
+}
+
+
+.algolia-autocomplete .algolia-docsearch-suggestion--text .algolia-docsearch-suggestion--highlight {
+ box-shadow: inset 0 -2px 0 0 rgba(105, 105, 105, .5)
+}
+
+.algolia-autocomplete .ds-suggestion.ds-cursor .algolia-docsearch-suggestion--content {
+ background-color: rgba(192, 192, 192, .15)
+}
diff --git a/docs/docsearch.js b/docs/docsearch.js
new file mode 100644
index 0000000..b35504c
--- /dev/null
+++ b/docs/docsearch.js
@@ -0,0 +1,85 @@
+$(function() {
+
+ // register a handler to move the focus to the search bar
+ // upon pressing shift + "/" (i.e. "?")
+ $(document).on('keydown', function(e) {
+ if (e.shiftKey && e.keyCode == 191) {
+ e.preventDefault();
+ $("#search-input").focus();
+ }
+ });
+
+ $(document).ready(function() {
+ // do keyword highlighting
+ /* modified from https://jsfiddle.net/julmot/bL6bb5oo/ */
+ var mark = function() {
+
+ var referrer = document.URL ;
+ var paramKey = "q" ;
+
+ if (referrer.indexOf("?") !== -1) {
+ var qs = referrer.substr(referrer.indexOf('?') + 1);
+ var qs_noanchor = qs.split('#')[0];
+ var qsa = qs_noanchor.split('&');
+ var keyword = "";
+
+ for (var i = 0; i < qsa.length; i++) {
+ var currentParam = qsa[i].split('=');
+
+ if (currentParam.length !== 2) {
+ continue;
+ }
+
+ if (currentParam[0] == paramKey) {
+ keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20"));
+ }
+ }
+
+ if (keyword !== "") {
+ $(".contents").unmark({
+ done: function() {
+ $(".contents").mark(keyword);
+ }
+ });
+ }
+ }
+ };
+
+ mark();
+ });
+});
+
+/* Search term highlighting ------------------------------*/
+
+function matchedWords(hit) {
+ var words = [];
+
+ var hierarchy = hit._highlightResult.hierarchy;
+ // loop to fetch from lvl0, lvl1, etc.
+ for (var idx in hierarchy) {
+ words = words.concat(hierarchy[idx].matchedWords);
+ }
+
+ var content = hit._highlightResult.content;
+ if (content) {
+ words = words.concat(content.matchedWords);
+ }
+
+ // return unique words
+ var words_uniq = [...new Set(words)];
+ return words_uniq;
+}
+
+function updateHitURL(hit) {
+
+ var words = matchedWords(hit);
+ var url = "";
+
+ if (hit.anchor) {
+ url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor;
+ } else {
+ url = hit.url + '?q=' + escape(words.join(" "));
+ }
+
+ return url;
+}
diff --git a/docs/index.html b/docs/index.html
new file mode 100644
index 0000000..2f111e2
--- /dev/null
+++ b/docs/index.html
@@ -0,0 +1,224 @@
+
+
+
+
+
+
+
+Utility functions to work with and extend the rbmi package • rbmiUtils
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+The goal of rbmiUtils is to provide additional functionality to {rbmi} for clinical trial analysis and reporting. This package is currently under development and is not yet available on CRAN.
+
+Local installation (for beta-testing only)
+
+You can install the latest version of rbmiUtils from GitHub:
+
+if (!require("remotes")) {
+ install.packages("remotes")
+}
+remotes::install_github("openpharma/rbmiUtils")
+After the official release, the package installation will differ.
+
+
+Example
+
+This is a basic example which shows you how to solve a common problem:
+
+# Example usage:
+library(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(rbmi)
+library(rbmiUtils)
+
+data("ADMI")
+N_IMPUTATIONS <- 100
+BURN_IN <- 200
+BURN_BETWEEN <- 5
+
+# Convert key columns to factors
+ADMI$TRT <- factor(ADMI$TRT, levels = c("Placebo", "Drug A"))
+ADMI$USUBJID <- factor(ADMI$USUBJID)
+ADMI$AVISIT <- factor(ADMI$AVISIT)
+
+# Define key variables for ANCOVA analysis
+ vars <- set_vars(
+ subjid = "USUBJID",
+ visit = "AVISIT",
+ group = "TRT",
+ outcome = "CHG",
+ covariates = c("BASE", "STRATA", "REGION") # Covariates for adjustment
+ )
+
+# Specify the imputation method (Bayesian) - need for pool step
+method <- rbmi::method_bayes(
+ n_samples = N_IMPUTATIONS,
+ burn_in = BURN_IN,
+ burn_between = BURN_BETWEEN
+)
+
+# Perform ANCOVA Analysis on Each Imputed Dataset
+ana_obj_ancova <- analyse_mi_data(
+ data = ADMI,
+ vars = vars,
+ method = method,
+ fun = ancova, # Apply ANCOVA
+ delta = NULL # No sensitivity analysis adjustment
+)
+
+pool_obj_ancova <- pool(ana_obj_ancova)
+tidy_df <- tidy_pool_obj(pool_obj_ancova)
+
+# Print tidy data frames
+print(tidy_df)
+#> # A tibble: 6 × 10
+#> parameter description visit parameter_type lsm_type est se lci
+#> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
+#> 1 trt_Week 24 Treatment … Week… trt <NA> -2.17 0.182 -2.53
+#> 2 lsm_ref_Week 24 Least Squa… Week… lsm ref 0.0782 0.131 -0.179
+#> 3 lsm_alt_Week 24 Least Squa… Week… lsm alt -2.09 0.126 -2.34
+#> 4 trt_Week 48 Treatment … Week… trt <NA> -3.81 0.256 -4.31
+#> 5 lsm_ref_Week 48 Least Squa… Week… lsm ref 0.0481 0.185 -0.316
+#> 6 lsm_alt_Week 48 Least Squa… Week… lsm alt -3.76 0.176 -4.11
+#> # ℹ 2 more variables: uci <dbl>, pval <dbl>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/link.svg b/docs/link.svg
new file mode 100644
index 0000000..88ad827
--- /dev/null
+++ b/docs/link.svg
@@ -0,0 +1,12 @@
+
+
+
diff --git a/docs/news/index.html b/docs/news/index.html
new file mode 100644
index 0000000..f9aee2e
--- /dev/null
+++ b/docs/news/index.html
@@ -0,0 +1,93 @@
+
+Changelog • rbmiUtils
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/pkgdown.css b/docs/pkgdown.css
new file mode 100644
index 0000000..80ea5b8
--- /dev/null
+++ b/docs/pkgdown.css
@@ -0,0 +1,384 @@
+/* Sticky footer */
+
+/**
+ * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/
+ * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css
+ *
+ * .Site -> body > .container
+ * .Site-content -> body > .container .row
+ * .footer -> footer
+ *
+ * Key idea seems to be to ensure that .container and __all its parents__
+ * have height set to 100%
+ *
+ */
+
+html, body {
+ height: 100%;
+}
+
+body {
+ position: relative;
+}
+
+body > .container {
+ display: flex;
+ height: 100%;
+ flex-direction: column;
+}
+
+body > .container .row {
+ flex: 1 0 auto;
+}
+
+footer {
+ margin-top: 45px;
+ padding: 35px 0 36px;
+ border-top: 1px solid #e5e5e5;
+ color: #666;
+ display: flex;
+ flex-shrink: 0;
+}
+footer p {
+ margin-bottom: 0;
+}
+footer div {
+ flex: 1;
+}
+footer .pkgdown {
+ text-align: right;
+}
+footer p {
+ margin-bottom: 0;
+}
+
+img.icon {
+ float: right;
+}
+
+/* Ensure in-page images don't run outside their container */
+.contents img {
+ max-width: 100%;
+ height: auto;
+}
+
+/* Fix bug in bootstrap (only seen in firefox) */
+summary {
+ display: list-item;
+}
+
+/* Typographic tweaking ---------------------------------*/
+
+.contents .page-header {
+ margin-top: calc(-60px + 1em);
+}
+
+dd {
+ margin-left: 3em;
+}
+
+/* Section anchors ---------------------------------*/
+
+a.anchor {
+ display: none;
+ margin-left: 5px;
+ width: 20px;
+ height: 20px;
+
+ background-image: url(./link.svg);
+ background-repeat: no-repeat;
+ background-size: 20px 20px;
+ background-position: center center;
+}
+
+h1:hover .anchor,
+h2:hover .anchor,
+h3:hover .anchor,
+h4:hover .anchor,
+h5:hover .anchor,
+h6:hover .anchor {
+ display: inline-block;
+}
+
+/* Fixes for fixed navbar --------------------------*/
+
+.contents h1, .contents h2, .contents h3, .contents h4 {
+ padding-top: 60px;
+ margin-top: -40px;
+}
+
+/* Navbar submenu --------------------------*/
+
+.dropdown-submenu {
+ position: relative;
+}
+
+.dropdown-submenu>.dropdown-menu {
+ top: 0;
+ left: 100%;
+ margin-top: -6px;
+ margin-left: -1px;
+ border-radius: 0 6px 6px 6px;
+}
+
+.dropdown-submenu:hover>.dropdown-menu {
+ display: block;
+}
+
+.dropdown-submenu>a:after {
+ display: block;
+ content: " ";
+ float: right;
+ width: 0;
+ height: 0;
+ border-color: transparent;
+ border-style: solid;
+ border-width: 5px 0 5px 5px;
+ border-left-color: #cccccc;
+ margin-top: 5px;
+ margin-right: -10px;
+}
+
+.dropdown-submenu:hover>a:after {
+ border-left-color: #ffffff;
+}
+
+.dropdown-submenu.pull-left {
+ float: none;
+}
+
+.dropdown-submenu.pull-left>.dropdown-menu {
+ left: -100%;
+ margin-left: 10px;
+ border-radius: 6px 0 6px 6px;
+}
+
+/* Sidebar --------------------------*/
+
+#pkgdown-sidebar {
+ margin-top: 30px;
+ position: -webkit-sticky;
+ position: sticky;
+ top: 70px;
+}
+
+#pkgdown-sidebar h2 {
+ font-size: 1.5em;
+ margin-top: 1em;
+}
+
+#pkgdown-sidebar h2:first-child {
+ margin-top: 0;
+}
+
+#pkgdown-sidebar .list-unstyled li {
+ margin-bottom: 0.5em;
+}
+
+/* bootstrap-toc tweaks ------------------------------------------------------*/
+
+/* All levels of nav */
+
+nav[data-toggle='toc'] .nav > li > a {
+ padding: 4px 20px 4px 6px;
+ font-size: 1.5rem;
+ font-weight: 400;
+ color: inherit;
+}
+
+nav[data-toggle='toc'] .nav > li > a:hover,
+nav[data-toggle='toc'] .nav > li > a:focus {
+ padding-left: 5px;
+ color: inherit;
+ border-left: 1px solid #878787;
+}
+
+nav[data-toggle='toc'] .nav > .active > a,
+nav[data-toggle='toc'] .nav > .active:hover > a,
+nav[data-toggle='toc'] .nav > .active:focus > a {
+ padding-left: 5px;
+ font-size: 1.5rem;
+ font-weight: 400;
+ color: inherit;
+ border-left: 2px solid #878787;
+}
+
+/* Nav: second level (shown on .active) */
+
+nav[data-toggle='toc'] .nav .nav {
+ display: none; /* Hide by default, but at >768px, show it */
+ padding-bottom: 10px;
+}
+
+nav[data-toggle='toc'] .nav .nav > li > a {
+ padding-left: 16px;
+ font-size: 1.35rem;
+}
+
+nav[data-toggle='toc'] .nav .nav > li > a:hover,
+nav[data-toggle='toc'] .nav .nav > li > a:focus {
+ padding-left: 15px;
+}
+
+nav[data-toggle='toc'] .nav .nav > .active > a,
+nav[data-toggle='toc'] .nav .nav > .active:hover > a,
+nav[data-toggle='toc'] .nav .nav > .active:focus > a {
+ padding-left: 15px;
+ font-weight: 500;
+ font-size: 1.35rem;
+}
+
+/* orcid ------------------------------------------------------------------- */
+
+.orcid {
+ font-size: 16px;
+ color: #A6CE39;
+ /* margins are required by official ORCID trademark and display guidelines */
+ margin-left:4px;
+ margin-right:4px;
+ vertical-align: middle;
+}
+
+/* Reference index & topics ----------------------------------------------- */
+
+.ref-index th {font-weight: normal;}
+
+.ref-index td {vertical-align: top; min-width: 100px}
+.ref-index .icon {width: 40px;}
+.ref-index .alias {width: 40%;}
+.ref-index-icons .alias {width: calc(40% - 40px);}
+.ref-index .title {width: 60%;}
+
+.ref-arguments th {text-align: right; padding-right: 10px;}
+.ref-arguments th, .ref-arguments td {vertical-align: top; min-width: 100px}
+.ref-arguments .name {width: 20%;}
+.ref-arguments .desc {width: 80%;}
+
+/* Nice scrolling for wide elements --------------------------------------- */
+
+table {
+ display: block;
+ overflow: auto;
+}
+
+/* Syntax highlighting ---------------------------------------------------- */
+
+pre, code, pre code {
+ background-color: #f8f8f8;
+ color: #333;
+}
+pre, pre code {
+ white-space: pre-wrap;
+ word-break: break-all;
+ overflow-wrap: break-word;
+}
+
+pre {
+ border: 1px solid #eee;
+}
+
+pre .img, pre .r-plt {
+ margin: 5px 0;
+}
+
+pre .img img, pre .r-plt img {
+ background-color: #fff;
+}
+
+code a, pre a {
+ color: #375f84;
+}
+
+a.sourceLine:hover {
+ text-decoration: none;
+}
+
+.fl {color: #1514b5;}
+.fu {color: #000000;} /* function */
+.ch,.st {color: #036a07;} /* string */
+.kw {color: #264D66;} /* keyword */
+.co {color: #888888;} /* comment */
+
+.error {font-weight: bolder;}
+.warning {font-weight: bolder;}
+
+/* Clipboard --------------------------*/
+
+.hasCopyButton {
+ position: relative;
+}
+
+.btn-copy-ex {
+ position: absolute;
+ right: 0;
+ top: 0;
+ visibility: hidden;
+}
+
+.hasCopyButton:hover button.btn-copy-ex {
+ visibility: visible;
+}
+
+/* headroom.js ------------------------ */
+
+.headroom {
+ will-change: transform;
+ transition: transform 200ms linear;
+}
+.headroom--pinned {
+ transform: translateY(0%);
+}
+.headroom--unpinned {
+ transform: translateY(-100%);
+}
+
+/* mark.js ----------------------------*/
+
+mark {
+ background-color: rgba(255, 255, 51, 0.5);
+ border-bottom: 2px solid rgba(255, 153, 51, 0.3);
+ padding: 1px;
+}
+
+/* vertical spacing after htmlwidgets */
+.html-widget {
+ margin-bottom: 10px;
+}
+
+/* fontawesome ------------------------ */
+
+.fab {
+ font-family: "Font Awesome 5 Brands" !important;
+}
+
+/* don't display links in code chunks when printing */
+/* source: https://stackoverflow.com/a/10781533 */
+@media print {
+ code a:link:after, code a:visited:after {
+ content: "";
+ }
+}
+
+/* Section anchors ---------------------------------
+ Added in pandoc 2.11: https://github.com/jgm/pandoc-templates/commit/9904bf71
+*/
+
+div.csl-bib-body { }
+div.csl-entry {
+ clear: both;
+}
+.hanging-indent div.csl-entry {
+ margin-left:2em;
+ text-indent:-2em;
+}
+div.csl-left-margin {
+ min-width:2em;
+ float:left;
+}
+div.csl-right-inline {
+ margin-left:2em;
+ padding-left:1em;
+}
+div.csl-indent {
+ margin-left: 2em;
+}
diff --git a/docs/pkgdown.js b/docs/pkgdown.js
new file mode 100644
index 0000000..6f0eee4
--- /dev/null
+++ b/docs/pkgdown.js
@@ -0,0 +1,108 @@
+/* http://gregfranko.com/blog/jquery-best-practices/ */
+(function($) {
+ $(function() {
+
+ $('.navbar-fixed-top').headroom();
+
+ $('body').css('padding-top', $('.navbar').height() + 10);
+ $(window).resize(function(){
+ $('body').css('padding-top', $('.navbar').height() + 10);
+ });
+
+ $('[data-toggle="tooltip"]').tooltip();
+
+ var cur_path = paths(location.pathname);
+ var links = $("#navbar ul li a");
+ var max_length = -1;
+ var pos = -1;
+ for (var i = 0; i < links.length; i++) {
+ if (links[i].getAttribute("href") === "#")
+ continue;
+ // Ignore external links
+ if (links[i].host !== location.host)
+ continue;
+
+ var nav_path = paths(links[i].pathname);
+
+ var length = prefix_length(nav_path, cur_path);
+ if (length > max_length) {
+ max_length = length;
+ pos = i;
+ }
+ }
+
+ // Add class to parent
+
+
+
+
+
+
+
+
+
+
+
+
+
+ The goal of rbmiUtils is to provide additional functionality to {rbmi} for clinical trial analysis and reporting. This package is currently under development and is not yet available on CRAN.
+
+
+
+Local installation (for beta-testing only) +
+You can install the latest version of rbmiUtils from GitHub:
+
+if (!require("remotes")) {
+ install.packages("remotes")
+}
+remotes::install_github("openpharma/rbmiUtils")After the official release, the package installation will differ.
+
+
+
+Example +
+This is a basic example which shows you how to solve a common problem:
+
+# Example usage:
+library(dplyr)
+#>
+#> Attaching package: 'dplyr'
+#> The following objects are masked from 'package:stats':
+#>
+#> filter, lag
+#> The following objects are masked from 'package:base':
+#>
+#> intersect, setdiff, setequal, union
+library(rbmi)
+library(rbmiUtils)
+
+data("ADMI")
+N_IMPUTATIONS <- 100
+BURN_IN <- 200
+BURN_BETWEEN <- 5
+
+# Convert key columns to factors
+ADMI$TRT <- factor(ADMI$TRT, levels = c("Placebo", "Drug A"))
+ADMI$USUBJID <- factor(ADMI$USUBJID)
+ADMI$AVISIT <- factor(ADMI$AVISIT)
+
+# Define key variables for ANCOVA analysis
+ vars <- set_vars(
+ subjid = "USUBJID",
+ visit = "AVISIT",
+ group = "TRT",
+ outcome = "CHG",
+ covariates = c("BASE", "STRATA", "REGION") # Covariates for adjustment
+ )
+
+# Specify the imputation method (Bayesian) - need for pool step
+method <- rbmi::method_bayes(
+ n_samples = N_IMPUTATIONS,
+ burn_in = BURN_IN,
+ burn_between = BURN_BETWEEN
+)
+
+# Perform ANCOVA Analysis on Each Imputed Dataset
+ana_obj_ancova <- analyse_mi_data(
+ data = ADMI,
+ vars = vars,
+ method = method,
+ fun = ancova, # Apply ANCOVA
+ delta = NULL # No sensitivity analysis adjustment
+)
+
+pool_obj_ancova <- pool(ana_obj_ancova)
+tidy_df <- tidy_pool_obj(pool_obj_ancova)
+
+# Print tidy data frames
+print(tidy_df)
+#> # A tibble: 6 × 10
+#> parameter description visit parameter_type lsm_type est se lci
+#> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
+#> 1 trt_Week 24 Treatment … Week… trt <NA> -2.17 0.182 -2.53
+#> 2 lsm_ref_Week 24 Least Squa… Week… lsm ref 0.0782 0.131 -0.179
+#> 3 lsm_alt_Week 24 Least Squa… Week… lsm alt -2.09 0.126 -2.34
+#> 4 trt_Week 48 Treatment … Week… trt <NA> -3.81 0.256 -4.31
+#> 5 lsm_ref_Week 48 Least Squa… Week… lsm ref 0.0481 0.185 -0.316
+#> 6 lsm_alt_Week 48 Least Squa… Week… lsm alt -3.76 0.176 -4.11
+#> # ℹ 2 more variables: uci <dbl>, pval <dbl>
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/reference/ADMI.html b/docs/reference/ADMI.html
new file mode 100644
index 0000000..8bbc624
--- /dev/null
+++ b/docs/reference/ADMI.html
@@ -0,0 +1,134 @@
+
+
+
+
+
+
+
+
+
+
+
+
+ Example efficacy trial dataset
+ +ADEFF.Rd
+
+
+ A simplified example of a simulated trial dataset, with missing data.
+
+
+
+
+ ADEFF
+
+
+ Format
+ADEFF
+A data frame with 1,000 rows and 10 columns:
- USUBJID +
Unique subject identifier
+
+- AVAL +
Primary outcome variable
+
+- TRT01P +
Planned treatment
+
+- STRATA +
Stratification at randomisation
+
+- REGION +
Stratification by region
+
+- REGIONC +
Stratification by region, numeric code
+
+- BASE +
Baseline value of primary outcome variable
+
+- CHG +
Change from baseline
+
+- AVISIT +
Visit number
+
+- PARAM +
Analysis parameter name
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/reference/analyse2.html b/docs/reference/analyse2.html
new file mode 100644
index 0000000..616c3f4
--- /dev/null
+++ b/docs/reference/analyse2.html
@@ -0,0 +1,140 @@
+
+
+
+
+
+
+
+
+
+
+
+
+ Example multiple imputation trial dataset
+ +ADMI.Rd
+
+
+ A simplified example of a simulated trial ADMI dataset
+
+
+
+
+ ADMI
+
+
+ Format
+ADMI
+A data frame with 100,000 rows and 12 columns:
- USUBJID +
Unique patient identifier
+
+- STRATA +
Stratification at randomisation
+
+- REGION +
Stratification by region
+
+- REGIONC +
Stratification by region, numeric code
+
+- TRT +
Planned treatment
+
+- BASE +
Baseline value of primary outcome variable
+
+- CHG +
Change from baseline
+
+- AVISIT +
Visit number
+
+- IMPID +
Imputation number identifier
+
+- CRIT1FLN +
Responder criteria (binary)
+
+- CRIT1FL +
Responder criteria (categorical)
+
+- CRIT +
Responder criteria (definition)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/reference/analyse_mi_data.html b/docs/reference/analyse_mi_data.html
new file mode 100644
index 0000000..5165581
--- /dev/null
+++ b/docs/reference/analyse_mi_data.html
@@ -0,0 +1,187 @@
+
+
+
+
+
+
+
+
+
+
+
+
+ Apply Analysis Function to Multiple Imputed Datasets
+ +analyse2.Rd
+
+
+ This function applies an analysis function to imputed datasets and stores the results for later pooling. It is designed to work with multiple imputed datasets and apply a given analysis function (e.g., ANCOVA) to each imputation iteration.
+
+
+
+
+ analyse2(
+ data = NULL,
+ vars = NULL,
+ method = NULL,
+ fun = rbmi::ancova,
+ delta = NULL,
+ ...
+)
+
+ Arguments
+ + +- data +
Data frame containing the imputed datasets. The data frame should include a variable that identifies distinct imputation iterations (e.g.,
IMPID).
+
+
+- vars +
A character vector specifying the variables of interest. Defaults to
NULL.
+
+
+- method +
Character string specifying the method used for analysis (e.g., "ANCOVA"). Defaults to
NULL.
+
+
+- fun +
Analysis function to be applied to each imputed dataset. Defaults to
ancova. Must be a function.
+
+
+- delta +
A
data.framecontaining additional information to adjust the analysis orNULLif not needed. Defaults toNULL.
+
+
+- ... +
Additional arguments passed to the analysis function
fun.
+
+
+
+ Value
+An object of rbmi class analysis that contains the results from applying the analysis function to each imputed dataset.
+
+
+ Details
+This function loops through distinct imputation datasets (identified by IMPID), applies the provided analysis function, and stores the results. The fun argument must be a valid function, and delta (if provided) must be either NULL or a data.frame.
+
+
+ Examples
+# Example usage with an ANCOVA function:
+# analyse2(data = imputed_data, fun = ancova)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/reference/as_analysis2.html b/docs/reference/as_analysis2.html
new file mode 100644
index 0000000..c2a29e5
--- /dev/null
+++ b/docs/reference/as_analysis2.html
@@ -0,0 +1,127 @@
+
+
+
+
+
+
+
+
+
+
+
+
+ Apply Analysis Function to Multiple Imputed Datasets
+ +analyse_mi_data.Rd
+
+
+ This function applies an analysis function (e.g., ANCOVA) to imputed datasets and stores the results for later pooling. It is designed to work with multiple imputed datasets and apply a given analysis function to each imputation iteration.
+
+
+
+
+ analyse_mi_data(
+ data = NULL,
+ vars = NULL,
+ method = NULL,
+ fun = rbmi::ancova,
+ delta = NULL,
+ ...
+)
+
+ Arguments
+ + +- data +
A data frame containing the imputed datasets. The data frame should include a variable (e.g.,
IMPID) that identifies distinct imputation iterations.
+
+
+- vars +
A list specifying key variables used in the analysis (e.g.,
subjid,visit,group,outcome). Required.
+
+
+- method +
A character string or object specifying the method used for analysis (e.g., Bayesian imputation). Defaults to
NULL.
+
+
+- fun +
A function that will be applied to each imputed dataset. Defaults to
rbmi::ancova. Must be a valid analysis function.
+
+
+- delta +
A
data.frameused for delta adjustments, orNULLif no delta adjustments are needed. Defaults toNULL.
+
+
+- ... +
Additional arguments passed to the analysis function
fun.
+
+
+
+ Value
+An object of class analysis containing the results from applying the analysis function to each imputed dataset.
+
+
+ Details
+The function loops through distinct imputation datasets (identified by IMPID), applies the provided analysis function fun, and stores the results for later pooling. If a delta dataset is provided, it will be merged with the imputed data to apply the specified delta adjustment before analysis.
+
+
+ Examples
+# Example usage with an ANCOVA function
+library(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(rbmi)
+
+data("ADMI")
+N_IMPUTATIONS <- 100
+BURN_IN <- 200
+BURN_BETWEEN <- 5
+
+# Convert key columns to factors
+ADMI$TRT <- factor(ADMI$TRT, levels = c("Placebo", "Drug A"))
+ADMI$USUBJID <- factor(ADMI$USUBJID)
+ADMI$AVISIT <- factor(ADMI$AVISIT)
+
+# Define key variables for ANCOVA analysis
+ vars <- set_vars(
+ subjid = "USUBJID",
+ visit = "AVISIT",
+ group = "TRT",
+ outcome = "CHG",
+ covariates = c("BASE", "STRATA", "REGION") # Covariates for adjustment
+ )
+
+# Specify the imputation method (Bayesian) - need for pool step
+method <- rbmi::method_bayes(
+ n_samples = N_IMPUTATIONS,
+ burn_in = BURN_IN,
+ burn_between = BURN_BETWEEN
+ )
+
+# Perform ANCOVA Analysis on Each Imputed Dataset
+ana_obj_ancova <- analyse_mi_data(
+ data = ADMI,
+ vars = vars,
+ method = method,
+ fun = ancova, # Apply ANCOVA
+ delta = NULL # No sensitivity analysis adjustment
+)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/reference/figures/README-pressure-1.png b/docs/reference/figures/README-pressure-1.png
new file mode 100644
index 0000000..7bc4c8f
Binary files /dev/null and b/docs/reference/figures/README-pressure-1.png differ
diff --git a/docs/reference/get_imputed_data.html b/docs/reference/get_imputed_data.html
new file mode 100644
index 0000000..c63b3bd
--- /dev/null
+++ b/docs/reference/get_imputed_data.html
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+
+
+
+
+
+
+
+
+
+ Construct an rbmi
+
+
+
+
+ Construct an rbmi analysis object
+
+ as_analysis2.Rd
+
+
+ This is a helper function to create an analysis object that stores the +results from multiple imputation analyses. It validates the results and +ensures proper class assignment.
+This is a modification of the rbmi::as_analysis function.
+
+
+
+
+ as_analysis2(results, method, delta = NULL, fun = NULL, fun_name = NULL)
+
+ Arguments
+ + +- results +
A list containing the analysis results for each imputation.
+
+
+- method +
The method object used for the imputation.
+
+
+- delta +
Optional. A delta dataset used for adjustment.
+
+
+- fun +
The analysis function that was used.
+
+
+- fun_name +
The name of the analysis function (used for printing).
+
+
+
+
+ Value
+An object of class analysis with the results and associated metadata.
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/reference/index.html b/docs/reference/index.html
new file mode 100644
index 0000000..8f0704b
--- /dev/null
+++ b/docs/reference/index.html
@@ -0,0 +1,111 @@
+
+
+
+
+
+
+
+
+
+
+
+
+ Get Imputed Data Sets as a data frame
+ +get_imputed_data.Rd
+
+
+ This function takes an imputed dataset and a mapping variable to return a dataset +with the original IDs mapped back and renamed appropriately.
+
+
+
+
+ get_imputed_data(impute_obj)
+
+ Arguments
+ + +
+
+
+ Value
+A data frame with the original subject IDs mapped and renamed.
+
+
+
+ Examples
+# Example usage:
+# result <- get_imputed_data(impute_obj)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/reference/rbmiUtils-package.html b/docs/reference/rbmiUtils-package.html
new file mode 100644
index 0000000..85b90ce
--- /dev/null
+++ b/docs/reference/rbmiUtils-package.html
@@ -0,0 +1,97 @@
+
+
+
+
+
+
+
+
+
+
+
+
+ Reference
+
+ All functions+ + |
+ |
|---|---|
| + + | +Example efficacy trial dataset |
+
| + + | +Example multiple imputation trial dataset |
+
| + + | +Apply Analysis Function to Multiple Imputed Datasets |
+
| + + | +Construct an rbmi |
+
| + + | +Get Imputed Data Sets as a data frame |
+
| + + | +Tidy and Annotate a Pooled Object for Publication |
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/reference/tidy_pool_obj.html b/docs/reference/tidy_pool_obj.html
new file mode 100644
index 0000000..001e9de
--- /dev/null
+++ b/docs/reference/tidy_pool_obj.html
@@ -0,0 +1,177 @@
+
+
+
+
+
+
+
+
+
+
+
+
+ rbmiUtils: Utility functions to work with and extend the rbmi package
+ +rbmiUtils-package.Rd
+
+
+
+
+
+ This package provides additional functionality for rbmi to enable trial analysis and reporting aligned with CDISC ADaM.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
new file mode 100644
index 0000000..50e35fb
--- /dev/null
+++ b/docs/sitemap.xml
@@ -0,0 +1,21 @@
+
+
+
+
+
+
+
+
+
+
+
+ Tidy and Annotate a Pooled Object for Publication
+ +tidy_pool_obj.Rd
+
+
+ This function processes a pooled analysis object of class pool into a tidy tibble format.
+It adds contextual information, such as whether a parameter is a treatment comparison or a least squares mean,
+dynamically identifies visit names from the parameter column, and provides additional columns for parameter type,
+least squares mean type, and visit.
+
+
+
+
+ tidy_pool_obj(pool_obj)
+
+ Value
+A tibble containing the processed pooled analysis results. The tibble includes columns for the parameter, +description, estimates, standard errors, confidence intervals, p-values, visit, parameter type, and least squares mean type.
+
+
+
+ Details
+The function rounds numeric columns to three decimal places for presentation. It dynamically processes
+the parameter column by separating it into components (e.g., type of estimate, reference vs. alternative arm, and visit),
+and provides informative descriptions in the output.
+
+
+ Examples
+# Example usage:
+library(dplyr)
+library(rbmi)
+
+data("ADMI")
+N_IMPUTATIONS <- 100
+BURN_IN <- 200
+BURN_BETWEEN <- 5
+
+# Convert key columns to factors
+ADMI$TRT <- factor(ADMI$TRT, levels = c("Placebo", "Drug A"))
+ADMI$USUBJID <- factor(ADMI$USUBJID)
+ADMI$AVISIT <- factor(ADMI$AVISIT)
+
+# Define key variables for ANCOVA analysis
+ vars <- set_vars(
+ subjid = "USUBJID",
+ visit = "AVISIT",
+ group = "TRT",
+ outcome = "CHG",
+ covariates = c("BASE", "STRATA", "REGION") # Covariates for adjustment
+ )
+
+# Specify the imputation method (Bayesian) - need for pool step
+method <- rbmi::method_bayes(
+ n_samples = N_IMPUTATIONS,
+ burn_in = BURN_IN,
+ burn_between = BURN_BETWEEN
+)
+
+# Perform ANCOVA Analysis on Each Imputed Dataset
+ana_obj_ancova <- analyse_mi_data(
+ data = ADMI,
+ vars = vars,
+ method = method,
+ fun = ancova, # Apply ANCOVA
+ delta = NULL # No sensitivity analysis adjustment
+)
+
+pool_obj_ancova <- pool(ana_obj_ancova)
+tidy_df <- tidy_pool_obj(pool_obj_ancova)
+
+# Print tidy data frames
+print(tidy_df)
+#> # A tibble: 6 × 10
+#> parameter description visit parameter_type lsm_type est se lci
+#> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
+#> 1 trt_Week 24 Treatment … Week… trt NA -2.17 0.182 -2.53
+#> 2 lsm_ref_Week 24 Least Squa… Week… lsm ref 0.0782 0.131 -0.179
+#> 3 lsm_alt_Week 24 Least Squa… Week… lsm alt -2.09 0.126 -2.34
+#> 4 trt_Week 48 Treatment … Week… trt NA -3.81 0.256 -4.31
+#> 5 lsm_ref_Week 48 Least Squa… Week… lsm ref 0.0481 0.185 -0.316
+#> 6 lsm_alt_Week 48 Least Squa… Week… lsm alt -3.76 0.176 -4.11
+#> # ℹ 2 more variables: uci <dbl>, pval <dbl>
+
+