96 bounds predprob

R/boundsPostprob.R

@@ -1,8 +1,10 @@
 #' Decision cutpoints for boundary (based on posterior probability)
 #'
+#' @description `r lifecycle::badge("experimental")`
+#'
 #' This function is used to identify the efficacy and futility
 #' boundaries based on the following rules:
-#' Efficacy boundary: find minimum x (xU) where Pr(RR > p1 |x, n, a, b) >= tU and
+#' Efficacy boundary: find minimum x (xU) where Pr(RR > p1 | x, n, a, b) >= tU and
 #' Futility boundary: find maximum x (xL) where Pr(RR < p0 | x, n, a, b) >= tL
 #'
 #' @inheritParams postprob
@@ -24,7 +26,7 @@
 #' @example examples/boundsPostprob.R
 #' @export
 boundsPostprob <- function(looks, p0, p1 = p0, tL, tU, parE = c(1, 1), weights) {
-  assert_numeric(looks)
+  assert_numeric(looks, any.missing = FALSE)
   assert_number(p0, lower = 0, upper = 1)
   assert_number(p1, lower = 0, upper = 1)
   assert_number(tL, lower = 0, upper = 1)
@@ -48,12 +50,12 @@ boundsPostprob <- function(looks, p0, p1 = p0, tL, tU, parE = c(1, 1), weights)
     xL <- NA
     xU <- NA
     for (x in 0:n) {
-      postp_fut <- 1 - postprob(x, n, p0, parE, weights) # futility look
+      postp_fut <- 1 - postprob(x = x, n = n, p = p0, parE = parE, weights = weights) # futility look
       if (postp_fut >= tL) { # Rule is P(RR < p0) > tL
         postL <- postp_fut
         xL <- x
       }
-      postp_eff <- postprob(x, n, p1, parE, weights) # efficacy look
+      postp_eff <- postprob(x = x, n = n, p = p1, parE = parE, weights = weights) # efficacy look
       if (postp_eff >= tU) { # Rule is P(RR > p1) > tU
         postU <- postp_eff
         xU <- x

R/boundsPredprob.R

@@ -1,42 +1,46 @@
 #' Decision cutpoints for boundary (based on predictive probability) for Decision 1 rule.
 #'
+#' @description `r lifecycle::badge("experimental")`
+#'
 #' This function is used to identify the efficacy boundary and futility
 #' boundary based on rules in @details.
 #'
 #' @inheritParams predprob
 #' @inheritParams ocPredprob
 #' @inheritParams boundsPostprob
 #' @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
+#' - `xL` : the maximum number of responses that meet the futility
+#'          threshold.
 #' - `pL` : response rate 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
+#' - `pL_upper_ci` : 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`
+#' - `predU` : predictive probability corresponding to `xU`.
 #' - `postU`: posterior probability corresponding to `xU`.
-#' - `LciU` : lower bound of one sided 95% CI for the response rate based on exact
+#' - `pU_lower_ci` : lower bound of one sided 95% CI for the response rate based on exact
 #'            binomial test.
 #'
 #' @importFrom stats binom.test
 #'
 #' @details see also `predprob()`
 #' The following rules are Decision 1 rules:
 #' Efficacy boundary: find minimum x (xU) where
-#' Pr(Pr(response rate > p0 | data) >= tT | x) >= phiU,
+#' Pr(Pr(RR > p0 | data) >= tT | x) >= phiU,
 #' Futility boundary: find maximum x (xL) where
-#' Pr(Pr(response rate > p0 | data) >= tT | x) =< phiL
+#' Pr(Pr(RR > p0 | data) >= tT | x) =< phiL
 #'
 #' @example examples/boundsPredprob.R
 #' @export
 #' @keywords graphics
 boundsPredprob <- function(looks, Nmax = max(looks), p0, tT, phiL, phiU, parE = c(1, 1), weights) {
-  assert_numeric(looks)
+  assert_numeric(looks, any.missing = FALSE)
   assert_number(p0, lower = 0, upper = 1)
   assert_number(tT, lower = 0, upper = 1)
+  assert_number(phiL, lower = 0, upper = 1)
+  assert_number(phiU, lower = 0, upper = 1)
   assert_numeric(parE, min.len = 2, any.missing = FALSE)
   znames <- c(
     "xL", "pL", "predL", "postL", "UciL",
@@ -45,18 +49,27 @@ boundsPredprob <- function(looks, Nmax = max(looks), p0, tT, phiL, phiU, parE =
   z <- matrix(NA, length(looks), length(znames))
   dimnames(z) <- list(looks, znames)
   k <- 0
+  assert_numeric(weights, min.len = 0, len = nrow(par), finite = TRUE)
   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) {
-      predprob <- predprob(x = x, n = n, Nmax = max(looks), p = p0, thetaT = tT, parE = parE, weights = weights)$result
-      if (predprob <= phiL) { # Futility look, Rule Pr(Pr(P > p0 | x, Y, a, b) >= tT | x) =< phiL
+      predprob <- predprob(
+        x = x,
+        n = n,
+        Nmax = max(looks),
+        p = p0,
+        thetaT = tT,
+        parE = parE,
+        weights = weights
+      )$result
+      if (predprob <= phiL) { # Futility look, Rule Pr(Pr( RR > p0 | x, Y) >= tT | x) =< phiL
         xL <- x
         predL <- predprob
       }
-      if (predprob >= phiU) { # Efficacy look, Rule Pr(Pr(P > p0 | x, Y, a, b) >= tT | x) >= phiU,
+      if (predprob >= phiU) { # Efficacy look, Rule Pr(Pr( RR > p0 | x, Y) >= tT | x) >= phiU,
         xU <- x
         predU <- predprob
         break
@@ -67,8 +80,8 @@ boundsPredprob <- function(looks, Nmax = max(looks), p0, tT, phiL, phiU, parE =
       xU <- NA
     }
     # calculate predictive and posterior probabilities at boundaries
-    postL <- postprob(xL, n, p0, parE = parE)
-    postU <- postprob(xU, n, p0, parE = parE)
+    postL <- postprob(x = xL, n = n, p = p0, parE = parE, weights = weights, log.p = FALSE)
+    postU <- postprob(x = xU, n = n, p = p0, parE = parE, weights = weights, log.p = FALSE)
     # 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)

R/ocPostprobDist.R

@@ -103,11 +103,11 @@ h_get_decisionDist <- function(nnr,
 #'
 #' Stop criteria for Efficacy :
 #'
-#' `Pr(truep > P_S + deltaE) > tU`
+#' `Pr(RR > P_S + deltaE) > tU`
 #'
 #' Stop criteria for Futility :
 #'
-#' `Pr(truep < P_S + deltaF) > tL`
+#' `Pr(RR < P_S + deltaF) > tL`
 #'
 #' Where `truep` is the assumed true rate of response and `p1` and `p0` respectively are
 #' the thresholds for Efficacy and Futility respectively.

R/ocPredprob.R

@@ -211,17 +211,17 @@ h_get_decision_two_predprob <- function(nnr, truep, p0, p1, parE = c(1, 1), nnE,
 #' - interim STOP = P(successful trial at final) < phiL
 #'
 #' The criteria for Decision 1 for Final looks are:
-#' - Final GO = P( response rate > p0 | data) > tT
-#' - Final STOP = P( response rate > p0 | data ) < tT
+#' - Final GO = P( RR > p0 | data) > tT
+#' - Final STOP = P( RR > p0 | data ) < tT
 #'
 #' ## Decision 2:
 #' The criteria for Decision 2 for Interim looks are :
 #' - Interim GO : P ( success at final) > phiU
 #' - Interim STOP : P (failure at final ) > phiFu
 #'
 #' The criteria for Decision 2 for Futility looks are :
-#' - Final GO = P( response rate > p0) > tT
-#' - Final STOP = P( response rate  < p1) > tF
+#' - Final GO = P( RR > p0) > tT
+#' - Final STOP = P( RR  < p1) > tF
 #'
 #' @inheritParams h_get_decision_one_predprob
 #' @inheritParams h_get_decision_two_predprob

examples/boundsPredprob.R

@@ -2,9 +2,9 @@
 # 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,
+# Efficacy look Pr(Pr(RR > p0 | x, Y) >= 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
+# Futility look Pr(Pr(RR) > p0 | x, Y) >= tT | x) =< phiL
 # Uniform prior (i.e. beta(1, 1)) on the ORR:
 boundsPredprob(
   looks = c(10, 20, 30, 40),
@@ -18,9 +18,9 @@ boundsPredprob(
 # 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,
+# Efficacy look Pr(Pr(RR > p0 | x, Y) >= 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
+# Futility look Pr(Pr(RR > p0 | x, Y) >= tT | x) =< phi
 # with mixed prior and weights:
 boundsPredprob(
   looks = c(7, 15, 25),

examples/ocPostprob.R

@@ -1,8 +1,8 @@
 # For three looks of 10, 20 and 30 we have the following assumptions :
 # True response rate or truep of the treatment group = 40%
 # The following are the Stop rules respectively :
-# Look for Efficacy: Pr(truep > 30% )> 80%
-# Look for Futility: Pr(truep < 20% )> 60%
+# Look for Efficacy: Pr(RR > 30% )> 80%
+# Look for Futility: Pr(RR < 20% )> 60%
 # Prior of treatment arm parE= Beta(1,1).
 res <- ocPostprob(
   nnE = c(10, 20, 30), truep = 0.40, p0 = 0.20, p1 = 0.30, tL = 0.60, tU = 0.80, parE = c(1, 1),

examples/ocPostprobDist.R

@@ -3,8 +3,6 @@
 # True response rate or truep of the treatment group = 40%
 # The following are the Go and Stop rules respectively :
 # Prior of treatment arm parE= Beta(1,1).
-# stop for efficacy (deltaE): Pr(truep > P_S + deltaE) > tU
-# stop for futility (deltaF): Pr(truep < P_S + deltaF) > tL
 # Without random distance allowed for Futility and Efficacy Looks.
 res1 <- ocPostprobDist(
   nnE = c(10, 20, 30),

examples/ocPredprob.R

@@ -1,8 +1,8 @@
 # Here we illustrate an example for Decision 1 with the following assumptions :
 # True response rate or truep of the treatment group = 40%
 # The following are the Final Stop rules respectively :
-# - Final look for Efficacy: Pr( response rate > 25% ) > 60% or P(response rate > p0) > tT
-# - Final look for Futility: Pr( response rate < 25% ) < 60% or P(response rate > p0) < tT
+# - Final look for Efficacy: Pr( RR > 25% ) > 60% or P( RR > p0) > tT
+# - Final look for Futility: Pr( RR < 25% ) < 60% or P(RR > 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.
@@ -76,8 +76,8 @@ result$oc
 # Here we illustrate an example for Decision 2 with the following assumptions :
 # True response rate or truep of the treatment group = 60%
 # The following are the Final Stop rules respectively :
-# - Final look for Efficacy: Pr( response rate > 25% ) > 60% or P(response rate > p0) > tT
-# - Final look for Futility: Pr( response rate < 25% ) < 60% or P(response rate < p1) > tF
+# - Final look for Efficacy: Pr( RR > 25% ) > 60% or P(RR > p0) > tT
+# - Final look for Futility: Pr( RR < 25% ) < 60% or P(RR < p1) > tF
 # - Interim look for Efficacy: Pr( success at final ) > 80% or P(success at final) > phiU
 # - Interim look for Futility: Pr( failure at final ) > 80% or P(failure at final) > phiFu
 # We assume a prior of treatment arm parE = Beta(1,1), unless otherwise indicated.

examples/ocPredprobDist.R

@@ -2,15 +2,11 @@
 # Efficacy Looks and Futility looks are identical at sample size of 10, 20 and 30.
 # True response rate or truep of the treatment group = 40%
 # Desired difference to Standard of Care for Efficacy and Futility = 10%
-# 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( success at final ) < 20% or P(success at final) < phiL
+# The following are the Final Stop rules respectively :
+# - Final look for Efficacy: Pr( RR + deltaE > 25% ) > 60%
+# - Final look for Futility: Pr( RR + deltaF < 25% ) < 60%
+# - Interim look for Efficacy: Pr( success at final ) > 80%
+# - Interim look for Futility: Pr( failure at final ) < 20%
 # We assume a prior of treatment arm parE = Beta(1,1), unless otherwise indicated.
 
 set.seed(20)
@@ -41,14 +37,10 @@ result$oc
 # Desired difference to Standard of Care for Efficacy and Futility is 10% and -10% respectively.
 # Grey zone occurs due to different posterior probability distribution in the Efficacy and Futility rules.
 # Delta calculation is absolute case. The following are the Final Stop rules respectively :
-# - Final look for Efficacy: Pr( response rate + deltaE > 25% ) > 60% or
-#                            P(response rate + deltaE > p0) > tT
-# - Final look for Futility: Pr( response rate + deltaF < 25% ) < 60% or
-#                            P(response rate + deltaF > p0) < tT
-# - Interim look for Efficacy: Pr( success at final ) > 80% or
-#                            P(success at final) > phiU
-# - Interim look for Futility: Pr( failure at final ) < 20% or
-#                           P(success at final) < phiL
+# - Final look for Efficacy: Pr( RR + deltaE > 25% ) > 60%
+# - Final look for Futility: Pr( RR + deltaF < 25% ) < 60%
+# - Interim look for Efficacy: Pr( success at final ) > 80%
+# - Interim look for Futility: Pr( failure at final ) < 20%
 # We assume a prior of treatment arm parE = Beta(1,1), unless otherwise indicated.
 #
 set.seed(20)
@@ -78,12 +70,9 @@ result$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 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
+# - Final look for Efficacy: Pr( RR + deltaE > 25% ) > 60% or P(RR + deltaE > p0) > tT
+# - Final look for Futility: Pr( RR + deltaF < 25% ) < 60% or P(RR + 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.