Revert "115 make website live" (#117)

Co-authored-by: 27856297+dependabot-preview[bot]@users.noreply.github.com <27856297+dependabot-preview[bot]@users.noreply.github.com>

NAMESPACE

@@ -6,7 +6,6 @@ export(dbetaMix)
 export(dbetabinom)
 export(dbetabinomMix)
 export(dbetadiff)
-export(myPlot)
 export(myPlotDiff)
 export(oc2)
 export(oc3)

R/boundsPostprob.R

@@ -1,38 +1,42 @@
 #' Decision cutpoints for boundary (based on posterior probability)
 #'
 #' This function is used to identify the efficacy and futility
-#' boundaries based on the following rules:
-#' Efficacy boundary: find minimum x (xU) where Pr(P > p1 |x, n, a, b) >= tU and
-#' Futility boundary: find maximum x (xL) where Pr(P < p0 | x, n, a, b) >= tL
+#' boundaries based on posterior probabilities, i.e.:
+#' Efficacy boundary: find minimum x (xU) where Pr(P>p0|x,n,a,b) >= tU and
+#' Futility boundary: find maximum x (xL) where Pr(P>p1|x,n,a,b) <= tL
 #'
-#' @inheritParams postprob
-#' @inheritParams ocPostprob
-#' @typed nvec : numeric
-#'  A vector of number of patients in each look.
-#' @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`.
-#' - `postL`: posterior probability corresponding to `xL`.
-#' - `pL_upper_ci` : upper bound of one sided 95% CI for the response rate `pL` based on an
-#'            exact binomial test.
-#' - `xU` : the minimal number of responses that meet the efficacy threshold.
-#' - `pU` : response rate corresponding to `xU`.
-#' - `postU` : posterior probability corresponding to `xU`.
-#' - `pU_lower_ci` : lower bound of one sided 95% CI for the response rate `pU` based on exact
-#'            binomial test.
+#' @param nvec a vector of number of patients
+#' @param p0 the efficacy threshold parameter in the postprob function
+#' @param p1 the futility threshold parameter in the postprob function
+#' (default = p0)
+#' @param tL futility boundary probability threshold
+#' @param tU efficacy boundary probability threshold
+#' @param a  the alpha parameter of the beta prior of treatment group
+#' @param b  the beta parameter of the 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), posterior probability
+#' (postL), 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), posterior probability (postU) and
+#' the lower bound of one sided 95% CI for the response rate based on exact
+#' binomial test (LciU).
+#'
+#' @importFrom stats binom.test
 #'
 #' @example examples/boundsPostprob.R
 #' @export
+#' @keywords graphics
 boundsPostprob <- function(nvec, p0, p1 = p0, tL, tU, a, b) {
   z <- matrix(NA, length(nvec), 6)
   dimnames(z) <- list(nvec, c(
     "xL", "pL", "postL",
     "xU", "pU", "postU"
   ))
   znames <- c(
-    "xL", "pL", "postL", "pL_upper_ci",
-    "xU", "pU", "postU", "pU_lower_ci"
+    "xL", "pL", "postL", "UciL",
+    "xU", "pU", "postU", "LciU"
   )
   z <- matrix(NA, length(nvec), length(znames))
   dimnames(z) <- list(nvec, znames)
@@ -43,31 +47,26 @@ boundsPostprob <- function(nvec, p0, p1 = p0, tL, tU, a, b) {
     xL <- NA
     xU <- NA
     for (x in 0:n) {
-      postp <- 1 - postprob(x, n, p0, parE = c(a, b)) # futility look
-      if (postp >= tL) {
-        postL <- postp
+      postp <- postprob(x, n, p1, parE = c(a, b))
+      if (postp <= tL) {
         xL <- x
       }
-      postp <- postprob(x, n, p0, parE = c(a, b)) # efficacy look
+      if (p0 != p1) {
+        postp <- postprob(x, n, p0, parE = c(a, b))
+      }
       if (postp >= tU) {
-        postU <- postp
         xU <- x
+        # done: leave innermost for loop
         break
       }
     }
+    # calculate posterior probabilities at boundaries
+    postL <- postprob(xL, n, p1, parE = c(a, b))
+    postU <- postprob(xU, n, p0, parE = c(a, b))
     # calculate lower CI at boundaries
-    pL_upper_ci <- ifelse(!is.na(xL), stats::binom.test(xL, n, alt = "less")$conf.int[2], NA)
-    pU_lower_ci <- ifelse(!is.na(xU), stats::binom.test(xU, n, alt = "greater")$conf.int[1], NA)
-    z[k, ] <- c(
-      xL,
-      xL / n,
-      postL,
-      pL_upper_ci,
-      xU,
-      xU / n,
-      postU,
-      pU_lower_ci
-    )
+    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, postL, UciL, xU, xU / n, postU, LciU)
   }
   return(round(data.frame(nvec, z), 4))
 }

examples/boundsPostprob.R

@@ -1,8 +1,10 @@
-# 40 pts trial with interim looks after each 10 pts.,
-# Efficacy decision if more than 60% probability to be above 20% ORR,
-# Futility decision if less than 60% probability to be below 20% ORR,
-# with uniform prior (i.e. beta(1, 1)) on the ORR:
+## 40 pts trial with interim looks after each 10 pts.,
+## efficacy decision if more than 90% probability to be above 20% ORR,
+## futility decision if less than 10% probability to be above 20% ORR,
+## with uniform prior (i.e. beta(1, 1)) on the ORR:
 boundsPostprob(
-  nvec = c(10, 20, 30, 40), p0 = 0.20, p1 = 0.2,
-  tL = 0.60, tU = 0.60, a = 1, b = 1
+  nvec = c(10, 20, 30, 40), p0 = 0.20,
+  tL = 0.10, tU = 0.90, a = 1, b = 1
 )
+## From this we see e.g. that at the third IA at 30 pts, we would stop for futility
+## if 5 or less patients responded, and for efficacy if 9 or more pts responded.