Allow ve = 0 in simBinomialSeasonalExact() for superiority null scenarios

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: gsDesign
-Version: 3.9.0.9005
+Version: 3.9.0.9006
 Title: Group Sequential Design
 Authors@R: c(
     person("Keaven", "Anderson", email = "keaven_anderson@merck.com", role = c("aut", "cre")),

NEWS.md

@@ -62,6 +62,11 @@
 - `simBinomialSeasonalExact()` now supports `usTime`/`lsTime` inputs and
   reports futility stopping probabilities (`futility_stop_rate` with
   `futility_mc_se`) in scenario summaries.
+- `simBinomialSeasonalExact()` now accepts `ve = 0` and `ve < 0`, allowing
+  null-hypothesis (`ve = 0`) and non-inferiority margin (`ve < 0`) scenarios.
+  Validation now requires only that `ve` values are finite and less than 1.
+  A feasibility check verifies that the implied experimental-arm event rates
+  (`control_event_rate * (1 - ve)`) remain in `[0, 1)` (#267).
 
 ## Bug fixes
 

R/simBinomialSeasonalExact.R

@@ -13,7 +13,10 @@
 #'
 #' @param gsD A `gsSurv` object with `test.type` 1 or 4.
 #' @param ve Numeric vector of vaccine efficacy (or prevention efficacy)
-#'   scenarios to simulate.
+#'   scenarios to simulate. Each value must be finite and less than 1.
+#'   `ve = 0` corresponds to equal event rates (superiority null); `ve < 0`
+#'   corresponds to experimental-arm event rates above control (non-inferiority
+#'   margin or harmful scenarios).
 #' @param nsim Integer scalar or vector giving the number of simulations per
 #'   element of `ve`.
 #' @param control_event_rate Numeric scalar or vector with control seasonal
@@ -105,8 +108,8 @@ simBinomialSeasonalExact <- function(
   if (!(gsD$test.type %in% c(1, 4))) {
     stop("gsD$test.type must be 1 or 4", call. = FALSE)
   }
-  if (!is.numeric(ve) || length(ve) < 1 || any(!is.finite(ve)) || any(ve <= 0) || any(ve >= 1)) {
-    stop("ve must be a numeric vector with values strictly between 0 and 1", call. = FALSE)
+  if (!is.numeric(ve) || length(ve) < 1 || any(!is.finite(ve)) || any(ve >= 1)) {
+    stop("ve must be a numeric vector with finite values less than 1", call. = FALSE)
   }
   if (!is.numeric(season_length) || length(season_length) != 1 || !is.finite(season_length) || season_length <= 0) {
     stop("season_length must be a positive scalar", call. = FALSE)
@@ -146,6 +149,10 @@ simBinomialSeasonalExact <- function(
       any(!is.finite(control_event_rate)) || any(control_event_rate <= 0) || any(control_event_rate >= 1)) {
     stop("control_event_rate must be a scalar or vector in (0, 1) with one value per ve scenario", call. = FALSE)
   }
+  experimental_event_rate <- control_event_rate * (1 - ve)
+  if (any(experimental_event_rate < 0) || any(experimental_event_rate >= 1)) {
+    stop("ve and control_event_rate imply experimental event rates outside [0, 1)", call. = FALSE)
+  }
 
   if (!is.logical(adaptive) || length(adaptive) < 1 || any(is.na(adaptive))) {
     stop("adaptive must be a logical vector with at least one value", call. = FALSE)

tests/testthat/test-independent-test-simBinomialSeasonalExact.R

@@ -54,7 +54,17 @@ test_that("simBinomialSeasonalExact validates core inputs", {
 
   expect_error(
     simBinomialSeasonalExact(gsD = design, ve = c(0.3, 1)),
-    "strictly between 0 and 1"
+    "finite values less than 1"
+  )
+  expect_no_error(
+    simBinomialSeasonalExact(gsD = design, ve = c(0, 0.3), nsim = c(1, 1))
+  )
+  expect_no_error(
+    simBinomialSeasonalExact(gsD = design, ve = c(-0.1, 0, 0.3), nsim = c(1, 1, 1))
+  )
+  expect_error(
+    simBinomialSeasonalExact(gsD = design, ve = -10, control_event_rate = 0.5),
+    "experimental event rates outside"
   )
 
   expect_error(