## ----setup, include = FALSE--------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----setup-pipeline, message = FALSE, warning = FALSE------------------------- library(rbmiUtils) library(rbmi) library(dplyr) data("ADMI", package = "rbmiUtils") ADMI <- ADMI |> mutate( TRT = factor(TRT, levels = c("Placebo", "Drug A")), USUBJID = factor(USUBJID), AVISIT = factor(AVISIT) ) vars <- set_vars( subjid = "USUBJID", visit = "AVISIT", group = "TRT", outcome = "CHG", covariates = c("BASE", "STRATA", "REGION") ) method <- method_bayes( n_samples = 100, control = control_bayes(warmup = 200, thin = 5) ) ana_obj <- analyse_mi_data(ADMI, vars, method, fun = ancova) pool_obj <- pool(ana_obj) ## ----describe-draws-code, eval = FALSE---------------------------------------- # data("ADEFF", package = "rbmiUtils") # ADEFF <- ADEFF |> # mutate( # TRT = factor(TRT01P, levels = c("Placebo", "Drug A")), # USUBJID = factor(USUBJID), # AVISIT = factor(AVISIT, levels = c("Week 24", "Week 48")) # ) # # vars <- set_vars( # subjid = "USUBJID", visit = "AVISIT", group = "TRT", # outcome = "CHG", covariates = c("BASE", "STRATA", "REGION") # ) # method <- method_bayes( # n_samples = 100, # control = control_bayes(warmup = 200, thin = 2) # ) # # dat <- ADEFF |> select(USUBJID, STRATA, REGION, TRT, BASE, CHG, AVISIT) # draws_obj <- draws(data = dat, vars = vars, method = method) # desc <- describe_draws(draws_obj) # print(desc) ## ----describe-imputation-code, eval = FALSE----------------------------------- # impute_obj <- impute( # draws_obj, # references = c("Placebo" = "Placebo", "Drug A" = "Placebo") # ) # desc <- describe_imputation(impute_obj) # print(desc) ## ----ard-base-vs-enriched, eval = requireNamespace("cards", quietly = TRUE)---- # Base ARD (no diagnostics) ard <- pool_to_ard(pool_obj) # Enriched ARD with MI diagnostics ard_enriched <- pool_to_ard(pool_obj, analysis_obj = ana_obj) ## ----ard-diagnostics, eval = requireNamespace("cards", quietly = TRUE)-------- ard_enriched |> dplyr::filter(stat_name %in% c("fmi", "lambda", "riv", "df.adjusted", "re")) |> dplyr::select(group1_level, variable_level, stat_name, stat)