fixing conflicts

R/boundsPredprob.R

@@ -10,18 +10,18 @@
 #' @inheritParams predprob
 #' @inheritParams ocPredprob
 #' @inheritParams boundsPostprob
-#' @return A matrix for each same size in `nvec`. For each sample size, the following is returned:
+#' @return A matrix for each same size in `looks`. 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`
+#' - `predL` : 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`.
+#' - `predU` : predictive probability 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.
 #'
@@ -30,29 +30,32 @@
 #' @example examples/boundsPredprob.R
 #' @export
 #' @keywords graphics
-boundsPredprob <- function(nvec, Nmax = max(nvec), p0, tT, phiL, phiU, a, b) {
+boundsPredprob <- function(looks, Nmax = max(looks), p0, tT, phiL, phiU, parE = c(1, 1), weights) {
+  assert_numeric(looks)
+  assert_number(p0, lower = 0, upper = 1)
+  assert_number(tT, lower = 0, upper = 1)
+  assert_numeric(parE, min.len = 2, any.missing = FALSE)
   znames <- c(
-    "xL", "pL", "ppL", "postL", "UciL",
-    "xU", "pU", "ppU", "postU", "LciU"
+    "xL", "pL", "predL", "postL", "UciL",
+    "xU", "pU", "predU", "postU", "LciU"
   )
-  z <- matrix(NA, length(nvec), length(znames))
-  dimnames(z) <- list(nvec, znames)
+  z <- matrix(NA, length(looks), length(znames))
+  dimnames(z) <- list(looks, znames)
   k <- 0
-  for (n in nvec) {
+  for (n in looks) {
     k <- k + 1
     # initialize so will return NA if 0 or n in "continue" region
     xL <- NA
     xU <- NA
     for (x in 0:n) {
-      pp <- predprob(x, n, Nmax, p0, tT, parE = c(a, b))$result
-      if (pp <= phiL) {
+      predprob <- predprob(x = x, n = n, Nmax = max(looks), p = p0, thetaT = tT, parE = parE, weights = weights)$result
+      if (predprob <= phiL) { # Futility look, Rule Pr(Pr(P > p0 | x, Y, a, b) >= tT | x) =< phiL
         xL <- x
-        ppL <- pp
+        predL <- predprob
       }
-      if (pp >= phiU) {
+      if (predprob >= phiU) { # Efficacy look, Rule Pr(Pr(P > p0 | x, Y, a, b) >= tT | x) >= phiU,
         xU <- x
-        ppU <- ppL
-        # done: leave innermost for loop
+        predU <- predprob
         break
       }
     }
@@ -61,25 +64,23 @@ boundsPredprob <- function(nvec, Nmax = max(nvec), p0, tT, phiL, phiU, a, b) {
       xU <- NA
     }
     # calculate predictive and posterior probabilities at boundaries
-    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))
+    postL <- postprob(xL, n, p0, parE = parE)
+    postU <- postprob(xU, n, p0, parE = parE)
     # 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)
+    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,
-      ppL,
+      predL,
       postL,
-      UciL,
+      pL_upper_ci,
       xU,
       xU / n,
-      ppU,
+      predU,
       postU,
-      LciU
+      pU_lower_ci
     )
   }
-  return(round(data.frame(nvec, z), 4))
+  round(data.frame(looks, z), 4)
 }

R/predprob.R

@@ -63,7 +63,7 @@ predprob <- function(x, n, Nmax, p, thetaT, parE = c(1, 1),
     betamixPost,
     dbetabinomMix(x = 0:m, m = m, par = par, weights = weights)
   )
-  assert_numeric(density, lower = 0, upper = 1, finite = TRUE, any.missing = FALSE)
+  assert_numeric(density, lower = 0, upper = 1 + .Machine$double.eps, finite = TRUE, any.missing = FALSE)
   assert_number(thetaT, lower = 0, upper = 1, finite = TRUE)
   # posterior probabilities to be above threshold p
   posterior <- postprob(x = x + c(0:m), n = Nmax, p = p, parE = parE, weights = weights)

examples/boundsPredprob.R

@@ -1,12 +1,33 @@
-# 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.
+# 40 pts trial with interim looks after each 10 patients.
+# 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 or
+# Efficacy look Pr(Pr(P > p0 | x, Y, a, b) >= tT | x) >= phiU,
+# Interim futility decision if less than 10% predictive probability or
+# Futility look Pr(Pr(P > p0 | x, Y, a, b) >= tT | x) =< phiL
 # 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
+  looks = c(10, 20, 30, 40),
+  p0 = 0.20,
+  tT = 0.80,
+  phiL = 0.60,
+  phiU = 0.90
+)
+
+# 25 pts trial with interim looks at 7 and 15 pts.
+# 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 or
+# Efficacy look Pr(Pr(P > p0 | x, Y, a, b) >= tT | x) >= phiU,
+# Interim futility decision if less than 60% predictive probability or
+# Futility look Pr(Pr(P > p0 | x, Y, a, b) >= tT | x) =< phi
+# with mixed prior and weights:
+boundsPredprob(
+  looks = c(7, 15, 25),
+  p0 = 0.20,
+  tT = 0.80,
+  phiL = 0.60,
+  phiU = 0.90,
+  parE = cbind(c(1, 1), c(3, 10)),
+  weights = c(0.2, 0.8)
 )
-# 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.

examples/ocPredprobDist.R

@@ -10,7 +10,7 @@
 # - Interim look for Efficacy:
 #   Pr( success at final ) > 80% or P(success at final) > phiU
 # - Interim look for Futility:
-#   Pr( failure at final ) < 20% or P(success at final) < phiL
+#   Pr( success at final ) < 20% or P(success at final) < phiL
 # We assume a prior of treatment arm parE = Beta(1,1), unless otherwise indicated.
 
 set.seed(20)
@@ -41,10 +41,14 @@ result$oc
 # Desired difference to Standard of Care for Efficacy and Futility is 10% and -10% respectively.
 # Grey zone occurs due to different posterior probability distribution in the Efficacy and Futility rules.
 # Delta calculation is absolute case. The following are the Final Stop rules respectively :
-# - Final look for Efficacy: Pr( response rate + deltaE > 25% ) > 60% or P(response rate + deltaE > p0) > tT
-# - Final look for Futility: Pr( response rate + deltaF < 25% ) < 60% or P(response rate + deltaF > p0) < tT
-# - Interim look for Efficacy: Pr( success at final ) > 80% or P(success at final) > phiU
-# - Interim look for Futility: Pr( failure at final ) < 20% or P(success at final) < phiL
+# - Final look for Efficacy: Pr( response rate + deltaE > 25% ) > 60% or
+#                            P(response rate + deltaE > p0) > tT
+# - Final look for Futility: Pr( response rate + deltaF < 25% ) < 60% or
+#                            P(response rate + deltaF > p0) < tT
+# - Interim look for Efficacy: Pr( success at final ) > 80% or
+#                            P(success at final) > phiU
+# - Interim look for Futility: Pr( failure at final ) < 20% or
+#                           P(success at final) < phiL
 # We assume a prior of treatment arm parE = Beta(1,1), unless otherwise indicated.
 #
 set.seed(20)
@@ -111,10 +115,10 @@ result$oc
 # True response rate or truep of the treatment group = 40%
 # Desired difference to Standard of Care for Efficacy and Futility = 50%
 # Delta calculation is relative case. The following are the Final Stop rules respectively :
-# - Final look for Efficacy: P( P_S + (1-P_S)*deltaE > 25% ) > 60% or P( P_S + (1-P_S)*deltaE > p0) > tT
-# - Final look for Futility: P( P_S + (1-P_S)*deltaEF < 25% ) < 60% or P( P_S + (1-P_S)*deltaF > p0) < tT
-# - Interim look for Efficacy: P( success at final ) > 80% or P(success at final) > phiU
-# - Interim look for Futility: P( failure at final ) < 20% or P(success at final) < phiL
+# - Final look for Efficacy: P( P_S + (1-P_S)*deltaE > 25% ) > 60%
+# - Final look for Futility: P( P_S + (1-P_S)*deltaEF < 25% ) < 60%
+# - Interim look for Efficacy: P( success at final ) > 80%
+# - Interim look for Futility: P( failure at final ) < 20%
 # We assume a prior of treatment arm parE = Beta(1,1), unless otherwise indicated.
 
 set.seed(20)

examples/plotBounds.R

@@ -1,9 +1 @@
 # examples
-plotBounds(boundsPredprob(
-  nvec = c(10, 20, 30, 40), p = 0.20, tT = 0.80,
-  phiL = 0.10, phiU = 0.90, a = 1, b = 1
-), yt = "x")
-plotBounds(boundsPredprob(
-  nvec = c(10, 20, 30, 40), p = 0.20, tT = 0.80,
-  phiL = 0.10, phiU = 0.90, a = 1, b = 1
-), yt = "p")