resolve conflict when rebasing feature off main

R/boundsPredprob.R

@@ -1,38 +1,36 @@
-#' Decision cutpoints for boundary (based on predictive probability)
+#' Decision cutpoints for boundary (based on predictive probability) for Decision 1 rule.
 #'
 #' This function is used to identify the efficacy boundary and futility
-#' boundary based on predictive probabilities, i.e.:
+#' boundary based on the following rules:
 #' Efficacy boundary: find minimum x (xU) where
-#' Pr(Pr(P > p | x, Y) >= tT | x) > phiU,
+#' Pr(Pr(P > p0 | x, Y, a, b) >= tT | x) >= phiU,
 #' Futility boundary: find maximum x (xL) where
-#' Pr(Pr(P > p | x, Y) >= tT | x) < phiL
+#' Pr(Pr(P > p0 | x, Y, a, b) >= tT | x) =< phiL
 #'
-#' @param nvec  a vector of number of patients
-#' @param Nmax  maximum number of patients at the end of the trial
-#' (default: maximum of \code{nvec})
-#' @param p  threshold on the response rate
-#' @param tT  threshold on the posterior probability to be above p
-#' @param phiL  futility boundary predictive probability threshold
-#' @param phiU  efficacy boundary predictive probability threshold
-#' @param a  the alpha parameter of a beta prior of treatment group
-#' @param b  the beta parameter of a beta prior of treatment group
-#' @return A matrix where for each sample size in \code{nvec}, this function
-#' returns the maximum number of responses that meet the futility
-#' threshold (xL), its corresponding response rate (pL), predictive probability
-#' (ppL) and posterior probability (postL), the upper bound of one
-#' sided 95% CI for the response rate based on an
-#' exact binomial test (UciL), and the same boundary parameters for efficacy:
-#' the minimal number of responses that meet the efficacy threshold (xU),
-#' the corresponding response rate (pU), predictive probability
-#' (ppL) and posterior probability (postU), the lower bound of one sided
-#' 95% CI for the response rate based on exact binomial test (LciU).
+#' @inheritParams predprob
+#' @inheritParams ocPredprob
+#' @inheritParams boundsPostprob
+#' @return A matrix for each same size in `nvec`. For each sample size, the following is returned:
+#' - `xL` : the maximum number of responses that meet the futility.
+#'          threshold
+#' - `pL` : response rate corresponding to `xL`.
+#' - `ppL` : predictive probability corresponding to `xL`
+#' - `postL`: posterior probability corresponding to `xL`.
+#' - `Ucil` : upper bound of one sided 95% CI for the response rate based on an
+#'            exact binomial test.
+#' - `xU` : the minimal number of responses that meet the efficacy threshold.
+#' - `pU` : response rate corresponding to `xU`.
+#' - `postL`: posterior probability corresponding to `xU`.
+#' - `ppU` : predictive probability corresponding to `xU`
+#' - `LciU` : lower bound of one sided 95% CI for the response rate based on exact
+#'            binomial test.
 #'
 #' @importFrom stats binom.test
 #'
 #' @example examples/boundsPredprob.R
 #' @export
 #' @keywords graphics
-boundsPredprob <- function(nvec, Nmax = max(nvec), p, tT, phiL, phiU, a, b) {
+boundsPredprob <- function(nvec, Nmax = max(nvec), p0, tT, phiL, phiU, a, b) {
   znames <- c(
     "xL", "pL", "ppL", "postL", "UciL",
     "xU", "pU", "ppU", "postU", "LciU"
@@ -46,29 +44,42 @@ boundsPredprob <- function(nvec, Nmax = max(nvec), p, tT, phiL, phiU, a, b) {
     xL <- NA
     xU <- NA
     for (x in 0:n) {
-      pp <- predprob(x, n, Nmax, p, tT, parE = c(a, b))$result
+      pp <- predprob(x, n, Nmax, p0, tT, parE = c(a, b))$result
       if (pp <= phiL) {
         xL <- x
+        ppL <- pp
       }
       if (pp >= phiU) {
         xU <- x
+        ppU <- ppL
         # done: leave innermost for loop
         break
       }
     }
-    # reset xU to NA if phiU=1 and n<Nmax
+    # reset xU to NA if phiU = 1 and n < Nmax
     if (n < Nmax && phiU == 1) {
       xU <- NA
     }
     # calculate predictive and posterior probabilities at boundaries
-    ppL <- predprob(xL, n, Nmax, p, tT, parE = c(a, b))$result
-    ppU <- predprob(xU, n, Nmax, p, tT, parE = c(a, b))$result
-    postL <- postprob(xL, n, p, parE = c(a, b))
-    postU <- postprob(xU, n, p, parE = c(a, b))
+    ppL <- predprob(xL, n, Nmax, p0, tT, parE = c(a, b))$result
+    ppU <- predprob(xU, n, Nmax, p0, tT, parE = c(a, b))$result
+    postL <- postprob(xL, n, p0, parE = c(a, b))
+    postU <- postprob(xU, n, p0, parE = c(a, b))
     # calculate lower CI at boundaries
     UciL <- ifelse(!is.na(xL), stats::binom.test(xL, n, alt = "less")$conf.int[2], NA)
     LciU <- ifelse(!is.na(xU), stats::binom.test(xU, n, alt = "greater")$conf.int[1], NA)
-    z[k, ] <- c(xL, xL / n, ppL, postL, UciL, xU, xU / n, ppU, postU, LciU)
+    z[k, ] <- c(
+      xL,
+      xL / n,
+      ppL,
+      postL,
+      UciL,
+      xU,
+      xU / n,
+      ppU,
+      postU,
+      LciU
+    )
   }
   return(round(data.frame(nvec, z), 4))
 }

examples/boundsPredprob.R

@@ -1,12 +1,12 @@
-## 40 pts trial with interim looks after each 10 pts.,
-## final efficacy decision if more than 80% probability to be above 20% ORR,
-## final futility decision otherwise.
-## Interim efficacy decision if more than 90% predictive probability reach this,
-## interim futility decision if less than 10% predictive probability.
-## Uniform prior (i.e. beta(1, 1)) on the ORR:
+# 40 pts trial with interim looks after each 10 pts.,
+# final efficacy decision if more than 80% probability to be above 20% ORR,
+# final futility decision otherwise.
+# Interim efficacy decision if more than 90% predictive probability reach this,
+# interim futility decision if less than 10% predictive probability.
+# Uniform prior (i.e. beta(1, 1)) on the ORR:
 boundsPredprob(
   nvec = c(10, 20, 30, 40), p = 0.20, tT = 0.80,
   phiL = 0.10, phiU = 0.90, a = 1, b = 1
 )
-## From this we see e.g. that at the first IA at 10 pts, we would stop for futility
-## if no patient responded, and for efficacy if 4 or more pts responded.
+# From this we see e.g. that at the first IA at 10 pts, we would stop for futility
+# if no patient responded, and for efficacy if 4 or more pts responded.

tests/testthat/test-boundsPredprob.R

@@ -0,0 +1,33 @@
+# boundsPostProb ----
+test_that("boundsPredprob gives correct result and list", {
+  result <- boundsPredprob(
+    nvec = c(10, 20, 30, 40),
+    p0 = 0.2,
+    tT = 0.80,
+    phiL = 0.10,
+    phiU = 0.90,
+    a = 1,
+    b = 1
+  )
+  expected <- data.frame(
+    list(
+      nvec = c(10, 20, 30, 40),
+      xL = c(0, 2, 5, 9),
+      pL = c(0, 0.1, 0.1667, 0.225),
+      postL = c(0.0859, 0.1787, 0.3931, 0.704),
+      UciL = c(0.2589, 0.2826, 0.319, 0.3598),
+      xU = c(4, 7, 9, 10),
+      pU = c(0.4, 0.35, 0.3, 0.25),
+      postU = c(0.9496, 0.9569, 0.9254, 0.8177),
+      LciU = c(0.15, 0.1773, 0.1663, 0.1424)
+    )
+  )
+  expect_equal(result$xL, c(0, 2, 5, 9))
+  expect_equal(result$pL, c(0, 0.1, 0.1667, 0.225))
+  expect_equal(result$postL, c(0.0859, 0.1787, 0.3931, 0.704))
+  expect_equal(result$UciL, c(0.2589, 0.2826, 0.319, 0.3598))
+  expect_equal(result$xU, c(4, 7, 9, 10))
+  expect_equal(result$pU, c(0.4, 0.35, 0.3, 0.25))
+  expect_equal(result$postU, c(0.9496, 0.9569, 0.9254, 0.8177))
+  expect_equal(result$LciU, c(0.15, 0.1773, 0.1663, 0.1424))
+})