Merge branch 'main' into 477-release-gsdesign2-v113

R/fixed_design_lf.R

@@ -46,7 +46,7 @@
 #' x %>% summary()
 #'
 #' # Example 2: given sample size and compute power
-#' x <- fixed_design_fh(
+#' x <- fixed_design_lf(
 #'   alpha = .025,
 #'   enroll_rate = define_enroll_rate(duration = 18, rate = 20),
 #'   fail_rate = define_fail_rate(

R/globals.R

@@ -57,7 +57,7 @@ utils::globalVariables(
       # From `gs_design_wlr()`
       c(
         "IF", "time", "event", "info", "info0", "theta", "bound",
-        "z", "n", "rate"
+        "z", "n", "rate", "delta", "sigma2"
       ),
       # From `gs_info_ahr()`
       c("analysis", "time", "theta", "info", "info0"),

R/gs_design_wlr.R

@@ -172,9 +172,18 @@ gs_design_wlr <- function(
                    interval = interval) %>%
     dplyr::select(-c(n, delta, sigma2))
 
+  # get the FA events given the FA analysis time
   final_event <- y$event[nrow(y)]
-  final_info <- max(y$info)
-  info_frac_by_time <- y$info / max(y$info)
+
+  # calculate the FA info according to different info_scale
+  # calculate the info_frac of planned analysis time provided by `analysis_time`
+  if(info_scale %in% c("h0_info", "h0_h1_info")) {
+    final_info <- max(y$info0)
+    info_frac_by_time <- y$info0 / final_info
+  } else {
+    final_info <- max(y$info)
+    info_frac_by_time <- y$info / final_info
+  }
 
   # if it is info frac driven group sequential design
   # relabel the analysis to FA, and back calculate IAs from FA
@@ -206,7 +215,8 @@ gs_design_wlr <- function(
                            enroll_rate = enroll_rate, fail_rate = fail_rate, ratio = ratio,
                            weight = weight, approx = approx,
                            final_info = final_info, next_time = next_time,
-                           input_info_frac = info_frac[n_analysis - i])$root
+                           input_info_frac = info_frac[n_analysis - i],
+                           info_scale = info_scale)$root
 
         y_ia <- gs_info_wlr(enroll_rate, fail_rate, ratio = ratio,
                             event = NULL, analysis_time = ia_time,
@@ -224,7 +234,8 @@ gs_design_wlr <- function(
                            enroll_rate = enroll_rate, fail_rate = fail_rate, ratio = ratio,
                            weight = weight, approx = approx,
                            final_info = final_info, next_time = next_time,
-                           input_info_frac = info_frac[n_analysis - i])$root
+                           input_info_frac = info_frac[n_analysis - i],
+                           info_scale = info_scale)$root
 
         y_ia <- gs_info_wlr(enroll_rate, fail_rate, ratio = ratio,
                             event = ia_event, analysis_time = NULL,
@@ -304,6 +315,7 @@ gs_design_wlr <- function(
   # analysis table
   analysis <- allout %>%
     select(analysis, time, n, event, ahr, theta, info, info0, info_frac) %>%
+    mutate(info_frac0 = info0 / max(info0)) %>%
     unique() %>%
     arrange(analysis)
 
@@ -335,25 +347,37 @@ gs_design_wlr <- function(
 # utility function to find the analysis time to get the planned/input info_frac
 find_time_by_info_frac <- function(x, enroll_rate, fail_rate, ratio, weight, approx,
                                    final_info, next_time,
-                                   input_info_frac){
+                                   input_info_frac,
+                                   info_scale){
+
   ia_info <- gs_info_wlr(analysis_time = x, event = NULL,
                          enroll_rate = enroll_rate, fail_rate = fail_rate,
                          weight = weight, approx = approx, ratio = ratio,
                          interval = c(.01, next_time))
 
-  ia_info_frac <- ia_info$info / final_info
+  if (info_scale %in% c("h0_info", "h0_h1_info")) {
+    ia_info_frac <- ia_info$info0 / final_info
+  } else {
+    ia_info_frac <- ia_info$info / final_info
+  }
+
   return(ia_info_frac - input_info_frac)
 }
 
 # utility function to find the event to get the planned/input info_frac
 find_event_by_info_frac <- function(x, enroll_rate, fail_rate, ratio, weight, approx,
                                     final_info, next_time,
-                                    input_info_frac){
+                                    input_info_frac,
+                                    info_scale){
   ia_info <- gs_info_wlr(analysis_time = NULL, event = x,
                          enroll_rate = enroll_rate, fail_rate = fail_rate,
                          weight = weight, approx = approx, ratio = ratio,
                          interval = c(.01, next_time))
+  if (info_scale %in% c("h0_info", "h0_h1_info")) {
+    ia_info_frac <- ia_info$info0 / final_info
+  } else {
+    ia_info_frac <- ia_info$info / final_info
+  }
 
-  ia_info_frac <- ia_info$info / final_info
   return(ia_info_frac - input_info_frac)
 }

R/summary.R

@@ -291,7 +291,7 @@ summary.gs_design <- function(object,
   # (2) decimals to be displayed for the analysis variables in (3)
   default_vars <- if (method == "rd") c("n", "rd", "info_frac") else c(
     "time", "n", "event", "ahr",
-    switch(method, ahr = "info_frac0", wlr = "info_frac", combo = "event_frac")
+    switch(method, ahr = "info_frac0", wlr = "info_frac0", combo = "event_frac")
   )
   default_decimals <- if (method == "rd") c(1, 4, 2) else c(1, 1, 1, 2, 2)