Merge pull request #47 from RConsortium/app_output_refine

Refine application output and ADRG updates

.gitignore

@@ -3,8 +3,6 @@
 .RData
 .Ruserdata
 pilot2wrappers.Rcheck/
-pilot2wrappers*.tar.gz
-pilot2wrappers*.tgz
 inst/doc
 .DS_Store
 rsconnect/

.renvignore

@@ -0,0 +1,9 @@
+.Rproj.user
+.Rhistory
+.Ruserdata
+inst/
+rsconnect/
+dev/
+vignettes/
+pkgdown/
+.devcontainer/

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: pilot2wrappers
 Type: Package
 Title: R Consortium R Submission Pilot 2
-Version: 0.4.0
+Version: 0.5.0
 Authors@R: c(
         person("Eric", "Nantz", email = "[email protected]", role = c("aut", "cre")),
         person("Yilong", "Zhang", role = c("aut")),
@@ -39,12 +39,14 @@ Imports:
     htmltools,
     pkgload,
     tippy,
-    markdown
+    markdown,
+    purrr
 Suggests: 
     devtools,
     testthat,
     knitr,
-    rmarkdown
+    rmarkdown,
+    pkglite
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)  

NEWS.md

@@ -1,3 +1,10 @@
+# pilot2wrappers 0.5.0
+
+- Hide p-values when a data filter is applied, and display only when no filters are applied. This addresses comments from FDA reviewers after they saw a preview of the application.
+- Add a new description in the App Information module regarding display of p-values.
+- Add another footnote to the primary and efficacy table displays regarding the p-value display.
+- Increase plot window height for KM module, improving readability
+
 # pilot2wrappers 0.4.0
 
 - Ensure column width of efficacy table's 95% CI header is wide enough to fit in a single row

R/eff_models.R

@@ -10,14 +10,15 @@
 #' @param data Source dataset (filtered by flags)
 #' @param var Variable on which model should be run
 #' @param wk Visit to be modeled
+#' @param show_pvalue Indicator to display p-values in table
 #'
 #' @return Formatted dataframe
 #' 
 #' @importFrom dplyr filter mutate case_when rowwise select bind_rows arrange
 #' @importFrom stats drop1 confint
 #' @export
 #'
-efficacy_models <- function(data, var=NULL, wk=NULL) {
+efficacy_models <- function(data, var=NULL, wk=NULL, show_pvalue = TRUE) {
   # Need to set contrasts to work for Type III SS. See analysis results metadata for
   # table 14-3.01. Reference for R here: https://www.r-bloggers.com/anova-%E2%80%93-type-iiiiii-ss-explained/
   op <- options(contrasts = c("contr.sum","contr.poly"))
@@ -55,7 +56,7 @@ efficacy_models <- function(data, var=NULL, wk=NULL) {
 
   # Pull it out into a table
   sect1 <- tibble::tibble(row_label=c('p-value(Dose Response) [1][2]'),
-                  `81` = c(num_fmt(ancova[2, 'Pr(>F)'], int_len=4, digits=3, size=12))
+                  `81` = ifelse(show_pvalue, c(num_fmt(ancova[2, 'Pr(>F)'], int_len=4, digits=3, size=12)), "Not Applicable")
   ) %>%
     pad_row()
 
@@ -81,7 +82,7 @@ efficacy_models <- function(data, var=NULL, wk=NULL) {
     rowwise() %>%
     # Create the display strings
     mutate(
-      p = num_fmt(p.value, int_len=4, digits=3, size=12),
+      p = ifelse(show_pvalue, num_fmt(p.value, int_len=4, digits=3, size=12), "Not Applicable"),
       diff_se = as.character(
         glue::glue('{num_fmt(estimate, int_len=2, digits=1, size=4)} ({num_fmt(SE, int_len=1, digits=2, size=4)})')
       ),

R/helpers.R

@@ -87,4 +87,23 @@ set_data_path <- function(path) {
   # set golem config option
   golem::amend_golem_config("adam_path", path, talkative = FALSE)
   invisible(TRUE)
+}
+
+#' check if a filter is active in a teal module
+#' 
+#' @param datasets instance of teal filtered datasets class
+#' 
+#' @return boolean, TRUE if a filter is applied, FALSE otherwise
+filter_active <- function(datasets) {
+  result <- FALSE
+  if (length(names(datasets$get_filter_state()) > 0)) {
+    filter_use <- purrr::map_lgl(names(datasets$get_filter_state()), ~{
+      # grab call of filter code
+      f_call <- datasets$get_call(.x)$filter
+      f_call != glue::glue("{.x}_FILTERED <- {.x}")
+    })
+    result <- any(filter_use)
+  }
+
+  return(result)
 }

R/tm_g_kmplot.R

@@ -9,7 +9,7 @@
 #' @importFrom shiny NS tagList plotOutput
 ui_g_kmplot <- function(id, datasets) {
   ns <- NS(id)
-  plotOutput(ns("plot"))
+  plotOutput(ns("plot"), height = "800px")
 }
 
 #' srv_g_kmplot Server Functions

R/tm_t_efficacy.R

@@ -49,7 +49,8 @@ ui_t_efficacy <- function(id, datasets) {
     tags$br(),
     tags$hr(),
     fluidRow(
-      h6(tags$i("Abbreviations: CI=Confidence Interval; LS=Least Squarses; SD=Standard Deviation"))
+      h6(tags$i("Abbreviations: CI=Confidence Interval; LS=Least Squares; SD=Standard Deviation")),
+      h6(tags$p("Statistical model and comparison p-values removed when applying data filters. Refer to the application information for additional details."))
     )
   )
 }
@@ -112,7 +113,7 @@ srv_t_efficacy <- function(input, output, session, datasets) {
       mutate(
         comp = "Study Drug vs. Placebo",
         mean = fmt_ci(estimate, lower, upper),
-        p = fmt_pval(p.value)
+        p = ifelse(filter_active(datasets), "Not Applicable", fmt_pval(p.value))
       ) %>%
       select(comp:p)
 
@@ -168,7 +169,7 @@ srv_t_efficacy <- function(input, output, session, datasets) {
     reactable(
       apr0ancova2,
       columns = collist,
-      defaultColDef = colDef(footerStyle = list(fontStyle = "itatlic"))
+      defaultColDef = colDef(footerStyle = list(fontStyle = "italic"))
     )
   })
 }

R/tm_t_primary.R

@@ -11,6 +11,7 @@ ui_t_primary <- function(id, datasets) {
   ns <- NS(id)
   tagList(
     uiOutput(ns("table")),
+    p("Statistical model and comparison p-values removed when applying data filters. Refer to the application information for additional details."),
     p("[1] Based on Analysis of covariance (ANCOVA) model with treatment and site group as factors and baseline value as a covariate."),
     p("[2] Test for a non-zero coefficient for treatment (dose) as a continuous variable."),
     p("[3] Pairwise comparison with treatment as a categorical variable: p-values without adjustment for multiple comparisons.")
@@ -61,7 +62,7 @@ srv_t_primary <- function(input, output, session, datasets) {
 
 
     ## -----------------------------------------------------------------------------------------------------------------------------------
-    model_portion <- efficacy_models(adas, 'CHG', 24)
+    model_portion <- efficacy_models(adas, 'CHG', 24, !filter_active(datasets))
 
 
     ## -----------------------------------------------------------------------------------------------------------------------------------

dev/03_deploy.R

@@ -27,7 +27,7 @@ devtools::build(path = "renv/cellar")
 devtools::build(path = "renv/cellar", binary = TRUE)
 
 # Install to renv library
-renv::install("renv/cellar/pilot2wrappers_0.2.0.tar.gz")
+renv::install(file.path(getwd(), "renv/cellar/pilot2wrappers_0.5.0.tar.gz"))
 
 # Deploy
 

inst/app/docs/about.md

@@ -1,7 +1,10 @@
-## Introduction
+# Introduction
 
 This application is intended for a pilot submission to the FDA composing of a Shiny application, as part of the [R Submissions Working Group](https://rconsortium.github.io/submissions-wg/) Pilot 2. The data sets and results displayed in the application originate from the [Pilot 1 project](https://rconsortium.github.io/submissions-wg/pilot-overall.html#pilot-1---common-analyses). Below is a brief description of the rest of the application:
 
+## Note on Dynamic Filters
+
+This application enables dynamic filtering of the input datasets used within each of the modules described below. When one or more filters are applied, the statistical estimates displayed in the table or visualization will dynamically update. However, since the statistical models and comparison methods were not powered for dynamic subgroups produced by these filters, p-values will be removed from the view until all filters are removed.
 ### Demographic Table
 
 In this interface, summary statistics associated with baseline clinical characteristics and other demographic factors is shown.
@@ -16,4 +19,4 @@ A summary table of the primary efficacy analysis is shown for each of the time p
 
 ### Efficacy Table
 
-A summary table of an additional efficacy analysis is shown for baseline and week 20. The efficacy variable (Glucose) was analzying using ANCOVA model with treatment and baseline value as covariates, comparing Placebo to Xanomeline High Dose.
+A summary table of an additional efficacy analysis is shown for baseline and week 20. The efficacy variable (Glucose) was analzying using ANCOVA model with treatment and baseline value as covariates, comparing Placebo to Xanomeline High Dose.

inst/golem-config.yml

@@ -1,6 +1,6 @@
 default:
   golem_name: pilot2wrappers
-  golem_version: 0.4.0
+  golem_version: 0.5.0
   app_prod: no
   adam_path: "datasets/adam"
 production:

inst/startup.R

@@ -1,6 +1,6 @@
 # Project Level Setup
 R_version <- "4.2.0"																                  # set up project R version
-snapshot  <- "2022-02-01" 									                          # set up snapshot date
+snapshot  <- "2022-09-01" 									                          # set up snapshot date
 teal_snapshot <- "2022-06-09"
 repos <- c(
   paste0("https://packagemanager.rstudio.com/cran/", snapshot),