From dff2ff6ce97831a3ce4873239a70cae17037a4dd Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Tue, 21 May 2024 17:38:31 +0200
Subject: [PATCH 01/36] delete helpers
---
R/helpers.R | 77 -----------------------------------
tests/testthat/test-helpers.R | 13 ------
2 files changed, 90 deletions(-)
delete mode 100644 R/helpers.R
delete mode 100644 tests/testthat/test-helpers.R
diff --git a/R/helpers.R b/R/helpers.R
deleted file mode 100644
index 5b7b9c41..00000000
--- a/R/helpers.R
+++ /dev/null
@@ -1,77 +0,0 @@
-#'
-#' Checking for scalar
-#'
-#' @param x the input
-#' @return Returns \code{TRUE} if \code{x} is a length one vector
-#' (i.e., a scalar)
-#'
-#' @keywords internal
-is.scalar <- function(x) {
- return(identical(length(x), 1L))
-}
-
-
-#' Predicate checking for a boolean option
-#'
-#' @param x the object being checked
-#' @return Returns \code{TRUE} if \code{x} is a length one logical vector (i.e., a
-#' scalar)
-#'
-#' @keywords internal
-is.bool <- function(x) {
- return(is.scalar(x) && is.logical(x))
-}
-
-#' Predicate checking for a probability
-#'
-#' @param x the object being checked
-#' @param bounds whether to include the bounds 0 and 1 (default)
-#' @return Returns \code{TRUE} if \code{x} is a probability
-#'
-#' @keywords internal
-is.probability <- function(x,
- bounds = TRUE) {
- return(is.scalar(x) &&
- if (bounds) {
- 0 <= x && 1 >= x
- } else {
- 0 < x && 1 > x
- })
-}
-
-#' Predicate checking for a probability range
-#'
-#' @param x the object being checked
-#' @param bounds whether to include the bounds 0 and 1 (default)
-#' @return Returns \code{TRUE} if \code{x} is a probability range
-#'
-#' @keywords internal
-is.probRange <- function(x,
- bounds = TRUE) {
- return(identical(length(x), 2L) &&
- x[1] < x[2] &&
- if (bounds) {
- 0 <= x[1] && 1 >= x[2]
- } else {
- 0 < x[1] && 1 > x[2]
- })
-}
-
-
-#' The Logit Function
-#'
-#' @description `r lifecycle::badge("experimental")`
-#'
-#' This just calculates the logit.
-#'
-#' @typed x: numeric
-#' probabilities to transform.
-#' @return The logit of `x`.
-#'
-#' @example examples/logit.R
-#' @export
-logit <- function(x) {
- assert_numeric(x, lower = 0, upper = 1, finite = TRUE)
-
- stats::qlogis(x)
-}
diff --git a/tests/testthat/test-helpers.R b/tests/testthat/test-helpers.R
deleted file mode 100644
index 332acd98..00000000
--- a/tests/testthat/test-helpers.R
+++ /dev/null
@@ -1,13 +0,0 @@
-test_that("logit works as expected", {
- probs <- seq(0, 1, length = 100)
- result <- logit(probs)
- expect_numeric(result, len = length(probs), sorted = TRUE, unique = TRUE)
- expect_identical(result[1], -Inf)
- expect_identical(result[100], Inf)
-})
-
-test_that("logit gives expected value for example value 0.2", {
- result <- logit(0.2)
- expected <- -1.386294
- expect_equal(result, expected, tolerance = 1e-4)
-})
From 37561fcab0a269e17ae1253a1b676bc086e871cb Mon Sep 17 00:00:00 2001
From: "27856297+dependabot-preview[bot]@users.noreply.github.com"
<27856297+dependabot-preview[bot]@users.noreply.github.com>
Date: Tue, 21 May 2024 15:53:55 +0000
Subject: [PATCH 02/36] [skip roxygen] [skip vbump] Roxygen Man Pages Auto
Update
---
DESCRIPTION | 1 -
man/is.bool.Rd | 19 -------------------
man/is.probRange.Rd | 20 --------------------
man/is.probability.Rd | 20 --------------------
man/is.scalar.Rd | 19 -------------------
man/logit.Rd | 23 -----------------------
6 files changed, 102 deletions(-)
delete mode 100644 man/is.bool.Rd
delete mode 100644 man/is.probRange.Rd
delete mode 100644 man/is.probability.Rd
delete mode 100644 man/is.scalar.Rd
delete mode 100644 man/logit.Rd
diff --git a/DESCRIPTION b/DESCRIPTION
index a7e3765b..bfdb50be 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -52,7 +52,6 @@ Collate:
'boundsPredprob.R'
'data.R'
'dbetabinom.R'
- 'helpers.R'
'oc2.R'
'oc3.R'
'ocPostprob.R'
diff --git a/man/is.bool.Rd b/man/is.bool.Rd
deleted file mode 100644
index 23ff5261..00000000
--- a/man/is.bool.Rd
+++ /dev/null
@@ -1,19 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/helpers.R
-\name{is.bool}
-\alias{is.bool}
-\title{Predicate checking for a boolean option}
-\usage{
-is.bool(x)
-}
-\arguments{
-\item{x}{the object being checked}
-}
-\value{
-Returns \code{TRUE} if \code{x} is a length one logical vector (i.e., a
-scalar)
-}
-\description{
-Predicate checking for a boolean option
-}
-\keyword{internal}
diff --git a/man/is.probRange.Rd b/man/is.probRange.Rd
deleted file mode 100644
index e27fd5f6..00000000
--- a/man/is.probRange.Rd
+++ /dev/null
@@ -1,20 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/helpers.R
-\name{is.probRange}
-\alias{is.probRange}
-\title{Predicate checking for a probability range}
-\usage{
-is.probRange(x, bounds = TRUE)
-}
-\arguments{
-\item{x}{the object being checked}
-
-\item{bounds}{whether to include the bounds 0 and 1 (default)}
-}
-\value{
-Returns \code{TRUE} if \code{x} is a probability range
-}
-\description{
-Predicate checking for a probability range
-}
-\keyword{internal}
diff --git a/man/is.probability.Rd b/man/is.probability.Rd
deleted file mode 100644
index c8335609..00000000
--- a/man/is.probability.Rd
+++ /dev/null
@@ -1,20 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/helpers.R
-\name{is.probability}
-\alias{is.probability}
-\title{Predicate checking for a probability}
-\usage{
-is.probability(x, bounds = TRUE)
-}
-\arguments{
-\item{x}{the object being checked}
-
-\item{bounds}{whether to include the bounds 0 and 1 (default)}
-}
-\value{
-Returns \code{TRUE} if \code{x} is a probability
-}
-\description{
-Predicate checking for a probability
-}
-\keyword{internal}
diff --git a/man/is.scalar.Rd b/man/is.scalar.Rd
deleted file mode 100644
index e8735c12..00000000
--- a/man/is.scalar.Rd
+++ /dev/null
@@ -1,19 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/helpers.R
-\name{is.scalar}
-\alias{is.scalar}
-\title{Checking for scalar}
-\usage{
-is.scalar(x)
-}
-\arguments{
-\item{x}{the input}
-}
-\value{
-Returns \code{TRUE} if \code{x} is a length one vector
-(i.e., a scalar)
-}
-\description{
-Checking for scalar
-}
-\keyword{internal}
diff --git a/man/logit.Rd b/man/logit.Rd
deleted file mode 100644
index 3eb2ad42..00000000
--- a/man/logit.Rd
+++ /dev/null
@@ -1,23 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/helpers.R
-\name{logit}
-\alias{logit}
-\title{The Logit Function}
-\usage{
-logit(x)
-}
-\arguments{
-\item{x}{(\code{numeric}):\cr probabilities to transform.}
-}
-\value{
-The logit of \code{x}.
-}
-\description{
-\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#experimental}{\figure{lifecycle-experimental.svg}{options: alt='[Experimental]'}}}{\strong{[Experimental]}}
-
-This just calculates the logit.
-}
-\examples{
-x <- seq(0.1, 0.9, 0.1)
-logit(x)
-}
From ab9ba06d6dabefa1d0a2f9d9e7c4dd3cb1a67299 Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Fri, 24 May 2024 13:48:38 +0200
Subject: [PATCH 03/36] clean
---
R/boundsPostprob.R | 48 +++++++----
man/boundsPostprob.Rd | 30 ++++---
man/ocPredprob.Rd | 121 +++++++++++++++++++++++++++
tests/testthat/test-boundsPostProb.R | 28 +++++++
4 files changed, 200 insertions(+), 27 deletions(-)
create mode 100644 tests/testthat/test-boundsPostProb.R
diff --git a/R/boundsPostprob.R b/R/boundsPostprob.R
index 09822eda..1493ec80 100644
--- a/R/boundsPostprob.R
+++ b/R/boundsPostprob.R
@@ -5,15 +5,11 @@
#' 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
#'
-#' @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
+#' @inheritParams postprob
+#' @inheritParams ocPostprob
+#' @typed nvec : numeric
+#' a vector of number of patients in each look.
+#' @return A matrix where for each sample size in `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
@@ -23,7 +19,19 @@
#' the lower bound of one sided 95% CI for the response rate based on exact
#' binomial test (LciU).
#'
-#' @importFrom stats binom.test
+#' 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` : posterior probability corresponding to `xL`.
+#' - `postL`: posterior probability.
+#' - `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` : the corresponding response rate.
+#' - `postU` : posterior probability.
+#' - `LciU` : lower bound of one sided 95% CI for the response rate based on exact
+#' binomial test.
+#'
#'
#' @example examples/boundsPostprob.R
#' @export
@@ -43,30 +51,36 @@ boundsPostprob <- function(nvec, p0, p1 = p0, tL, tU, a, b) {
k <- 0
for (n in nvec) {
k <- k + 1
- # initialize so will return NA if 0 or n in "continue" region
xL <- NA
xU <- NA
for (x in 0:n) {
postp <- postprob(x, n, p1, parE = c(a, b))
if (postp <= tL) {
xL <- x
+ postL <- postp
}
if (p0 != p1) {
postp <- postprob(x, n, p0, parE = c(a, b))
}
if (postp >= tU) {
xU <- x
- # done: leave innermost for loop
- break
+ postU <- postp
+ break # needed ?
}
}
- # 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
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)
+ z[k, ] <- c(
+ xL,
+ xL / n,
+ postL,
+ UciL,
+ xU,
+ xU / n,
+ postU,
+ LciU
+ )
}
return(round(data.frame(nvec, z), 4))
}
diff --git a/man/boundsPostprob.Rd b/man/boundsPostprob.Rd
index 1cb2ae60..a26478ff 100644
--- a/man/boundsPostprob.Rd
+++ b/man/boundsPostprob.Rd
@@ -7,20 +7,15 @@
boundsPostprob(nvec, p0, p1 = p0, tL, tU, a, b)
}
\arguments{
-\item{nvec}{a vector of number of patients}
+\item{nvec}{(\code{numeric}):\cr a vector of number of patients in each look.}
-\item{p0}{the efficacy threshold parameter in the postprob function}
+\item{p0}{(\code{number}):\cr lower Futility threshold of response rate.}
-\item{p1}{the futility threshold parameter in the postprob function
-(default = p0)}
+\item{p1}{(\code{number}):\cr upper Efficacy threshold of response rate.}
-\item{tL}{futility boundary probability threshold}
+\item{tL}{(\code{number}):\cr posterior probability threshold for being below \code{p0}.}
-\item{tU}{efficacy boundary probability threshold}
-
-\item{a}{the alpha parameter of the beta prior of treatment group}
-
-\item{b}{the beta parameter of the beta prior of treatment group}
+\item{tU}{(\code{number}):\cr posterior probability threshold for being above \code{p1}.}
}
\value{
A matrix where for each sample size in \code{nvec}, this function
@@ -32,6 +27,21 @@ 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).
+
+A matrix for each same size in \code{nvec}. For each sample size, the following is returned:
+\itemize{
+\item \code{xL} : the maximum number of responses that meet the futility.
+threshold
+\item \code{pL} : posterior probability corresponding to \code{xL}.
+\item \code{postL}: posterior probability.
+\item \code{Ucil} : upper bound of one sided 95\% CI for the response rate based on an
+exact binomial test.
+\item \code{xU} : the minimal number of responses that meet the efficacy threshold.
+\item \code{pU} : the corresponding response rate.
+\item \code{postU} : posterior probability.
+\item \code{LciU} : lower bound of one sided 95\% CI for the response rate based on exact
+binomial test.
+}
}
\description{
This function is used to identify the efficacy and futility
diff --git a/man/ocPredprob.Rd b/man/ocPredprob.Rd
index 25c8d9e2..fa79aa7c 100644
--- a/man/ocPredprob.Rd
+++ b/man/ocPredprob.Rd
@@ -117,3 +117,124 @@ The criteria for Decision 2 for Futility looks are :
}
}
}
+\examples{
+# 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
+# - 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.
+
+# Decision 1 with no wiggle.
+set.seed(20)
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.4,
+ p0 = 0.25,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 100,
+ wiggle = FALSE,
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with wiggle.
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.4,
+ p0 = 0.25,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = c(10, 20),
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with separate Futility and Efficacy looks at interim and final without wiggle.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.4,
+ p0 = 0.25,
+ p1 = 0.2,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = FALSE,
+ nnF = c(10, 15, 20),
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with separate Futility and Efficacy looks at interim and final with wiggle.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.4,
+ p0 = 0.25,
+ p1 = 0.2,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = c(10, 15, 20),
+ decision1 = TRUE
+)
+result$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
+# - 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.
+
+# Decision 2 without wiggle.
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.6,
+ p0 = 0.25,
+ p1 = 0.25,
+ tT = 0.6,
+ tF = 0.6,
+ phiU = 0.8,
+ phiFu = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = FALSE,
+ nnF = c(10, 20),
+ decision1 = FALSE
+)
+result$oc
+
+# Decision 2 with wiggle and with Futility only at final with non-uniform beta prior parE.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.6,
+ p0 = 0.25,
+ p1 = 0.25,
+ tT = 0.6,
+ tF = 0.6,
+ phiL = 0.8,
+ phiU = 0.8,
+ parE = c(11, 19),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = 30,
+ decision1 = FALSE
+)
+result$oc
+}
diff --git a/tests/testthat/test-boundsPostProb.R b/tests/testthat/test-boundsPostProb.R
new file mode 100644
index 00000000..0ae6eadf
--- /dev/null
+++ b/tests/testthat/test-boundsPostProb.R
@@ -0,0 +1,28 @@
+# boundsPostProb ----
+test_that("h_decision_one_RctPredProbDist gives correct result and list when relativeDelta = TRUE", {
+ result <- boundsPostprob(
+ nvec = c(10, 20, 30, 40),
+ p0 = 0.2,
+ p1 = 0.2,
+ tL = 0.10,
+ tU = 0.90,
+ a = 1,
+ b = 1
+ )
+ expected <- data.frame(
+ list(
+ nvec = c(10, 20, 30, 40),
+ xL = c(0, 1, 2, 4),
+ pL = c(0, 0.05, 0.0667, 0.1),
+ postL = c(0.0859, 0.0576, 0.0374, 0.0664),
+ UciL = c(0.2589, 0.2161, 0.1953, 0.2144),
+ xU = c(4, 7, 9, 12),
+ pU = c(0.4, 0.35, 0.3, 0.3),
+ postU = c(0.9496, 0.9569, 0.9254, 0.9479),
+ LciU = c(0.15, 0.1773, 0.1663, 0.1831)
+ )
+ )
+ expect_equal(result$xL, c(0, 1, 2, 4))
+ expect_equal(result$pL, c(0.0000, 0.0500, 0.0667, 0.1000))
+ expect_equal(result$UciL, c(0.2589, 0.2161, 0.1953, 0.2144))
+})
From ab510302661d18d4368c4f46c358b7565a643525 Mon Sep 17 00:00:00 2001
From: "27856297+dependabot-preview[bot]@users.noreply.github.com"
<27856297+dependabot-preview[bot]@users.noreply.github.com>
Date: Fri, 24 May 2024 11:51:21 +0000
Subject: [PATCH 04/36] [skip roxygen] [skip vbump] Roxygen Man Pages Auto
Update
---
man/ocPredprob.Rd | 121 ----------------------------------------------
1 file changed, 121 deletions(-)
diff --git a/man/ocPredprob.Rd b/man/ocPredprob.Rd
index fa79aa7c..25c8d9e2 100644
--- a/man/ocPredprob.Rd
+++ b/man/ocPredprob.Rd
@@ -117,124 +117,3 @@ The criteria for Decision 2 for Futility looks are :
}
}
}
-\examples{
-# Here we illustrate an example for Decision 1 with the following assumptions :
-# True response rate or truep of the treatment group = 40\%
-# The following are the Final Stop rules respectively :
-# - Final look for Efficacy: Pr( response rate > 25\% ) > 60\% or P(response rate > p0) > tT
-# - Final look for Futility: Pr( response rate < 25\% ) < 60\% or P(response rate > p0) < tT
-# - Interim look for Efficacy: Pr( success at final ) > 80\% or P(success at final) > phiU
-# - Interim look for Futility: Pr( failure at final ) < 20\% or P(success at final) < phiL
-# We assume a prior of treatment arm parE = Beta(1,1), unless otherwise indicated.
-
-# Decision 1 with no wiggle.
-set.seed(20)
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.4,
- p0 = 0.25,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 100,
- wiggle = FALSE,
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with wiggle.
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.4,
- p0 = 0.25,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = TRUE,
- nnF = c(10, 20),
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with separate Futility and Efficacy looks at interim and final without wiggle.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.4,
- p0 = 0.25,
- p1 = 0.2,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = FALSE,
- nnF = c(10, 15, 20),
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with separate Futility and Efficacy looks at interim and final with wiggle.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.4,
- p0 = 0.25,
- p1 = 0.2,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = TRUE,
- nnF = c(10, 15, 20),
- decision1 = TRUE
-)
-result$oc
-
-# Here we illustrate an example for Decision 2 with the following assumptions :
-# True response rate or truep of the treatment group = 60\%
-# The following are the Final Stop rules respectively :
-# - Final look for Efficacy: Pr( response rate > 25\% ) > 60\% or P(response rate > p0) > tT
-# - Final look for Futility: Pr( response rate < 25\% ) < 60\% or P(response rate < p1) > tF
-# - Interim look for Efficacy: Pr( success at final ) > 80\% or P(success at final) > phiU
-# - Interim look for Futility: Pr( failure at final ) > 80\% or P(failure at final) > phiFu
-# We assume a prior of treatment arm parE = Beta(1,1), unless otherwise indicated.
-
-# Decision 2 without wiggle.
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.6,
- p0 = 0.25,
- p1 = 0.25,
- tT = 0.6,
- tF = 0.6,
- phiU = 0.8,
- phiFu = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = FALSE,
- nnF = c(10, 20),
- decision1 = FALSE
-)
-result$oc
-
-# Decision 2 with wiggle and with Futility only at final with non-uniform beta prior parE.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.6,
- p0 = 0.25,
- p1 = 0.25,
- tT = 0.6,
- tF = 0.6,
- phiL = 0.8,
- phiU = 0.8,
- parE = c(11, 19),
- sim = 50,
- wiggle = TRUE,
- nnF = 30,
- decision1 = FALSE
-)
-result$oc
-}
From 008502dd2c31565eb6ea5e1942897da76afe4102 Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Fri, 24 May 2024 14:00:43 +0200
Subject: [PATCH 05/36] clean
---
R/boundsPostprob.R | 13 +++++++------
man/boundsPostprob.Rd | 13 ++++++++-----
2 files changed, 15 insertions(+), 11 deletions(-)
diff --git a/R/boundsPostprob.R b/R/boundsPostprob.R
index 1493ec80..f1f32fc8 100644
--- a/R/boundsPostprob.R
+++ b/R/boundsPostprob.R
@@ -5,6 +5,9 @@
#' 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
#'
+#' 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
@@ -22,20 +25,18 @@
#' 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` : posterior probability corresponding to `xL`.
-#' - `postL`: posterior probability.
+#' - `pL` : response rate 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` : the corresponding response rate.
-#' - `postU` : posterior probability.
+#' - `pU` : response rate corresponding to `xU`.
+#' - `postU` : posterior probability corresponding to `xU`.
#' - `LciU` : lower bound of one sided 95% CI for the response rate based on exact
#' binomial 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(
diff --git a/man/boundsPostprob.Rd b/man/boundsPostprob.Rd
index a26478ff..860636e2 100644
--- a/man/boundsPostprob.Rd
+++ b/man/boundsPostprob.Rd
@@ -32,13 +32,13 @@ A matrix for each same size in \code{nvec}. For each sample size, the following
\itemize{
\item \code{xL} : the maximum number of responses that meet the futility.
threshold
-\item \code{pL} : posterior probability corresponding to \code{xL}.
-\item \code{postL}: posterior probability.
+\item \code{pL} : response rate corresponding to \code{xL}.
+\item \code{postL}: posterior probability corresponding to \code{xL}.
\item \code{Ucil} : upper bound of one sided 95\% CI for the response rate based on an
exact binomial test.
\item \code{xU} : the minimal number of responses that meet the efficacy threshold.
-\item \code{pU} : the corresponding response rate.
-\item \code{postU} : posterior probability.
+\item \code{pU} : response rate corresponding to \code{xU}.
+\item \code{postU} : posterior probability corresponding to \code{xU}.
\item \code{LciU} : lower bound of one sided 95\% CI for the response rate based on exact
binomial test.
}
@@ -49,6 +49,10 @@ 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
}
+\details{
+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
+}
\examples{
## 40 pts trial with interim looks after each 10 pts.,
## efficacy decision if more than 90\% probability to be above 20\% ORR,
@@ -61,4 +65,3 @@ boundsPostprob(
## 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.
}
-\keyword{graphics}
From 34a2313c0cb2011300a200919c1f8e2e929229e1 Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Fri, 24 May 2024 14:51:35 +0200
Subject: [PATCH 06/36] empty
From 0cd95a75ad572ad32d77147a31485cac1156703e Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Sun, 26 May 2024 18:08:04 +0200
Subject: [PATCH 07/36] examples and test there
---
R/boundsPostprob.R | 37 +++-----
examples/boundsPostprob.R | 14 ++--
man/boundsPostprob.Rd | 34 +++-----
man/ocPredprob.Rd | 121 +++++++++++++++++++++++++++
tests/testthat/test-boundsPostProb.R | 31 ++++---
5 files changed, 167 insertions(+), 70 deletions(-)
diff --git a/R/boundsPostprob.R b/R/boundsPostprob.R
index f1f32fc8..f7b5415d 100644
--- a/R/boundsPostprob.R
+++ b/R/boundsPostprob.R
@@ -1,28 +1,16 @@
#' Decision cutpoints for boundary (based on posterior probability)
#'
#' This function is used to identify the efficacy and futility
-#' 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
+#' boundaries based on the following rules:
#'
-#' 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
+#' 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
#'
#' @inheritParams postprob
#' @inheritParams ocPostprob
#' @typed nvec : numeric
-#' a vector of number of patients in each look.
-#' @return A matrix where for each sample size in `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).
-#'
-#' A matrix for each same size in `nvec`. For each sample size, the following is returned:
+#' 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`.
@@ -52,21 +40,20 @@ boundsPostprob <- function(nvec, p0, p1 = p0, tL, tU, a, b) {
k <- 0
for (n in nvec) {
k <- k + 1
+ # initialize so will return NA if 0 or n in "continue" region
xL <- NA
xU <- NA
for (x in 0:n) {
- postp <- postprob(x, n, p1, parE = c(a, b))
- if (postp <= tL) {
- xL <- x
+ postp <- 1 - postprob(x, n, p0, parE = c(a, b)) # futility look
+ if (postp >= tL) {
postL <- postp
+ xL <- x
}
- if (p0 != p1) {
- postp <- postprob(x, n, p0, parE = c(a, b))
- }
+ postp <- postprob(x, n, p0, parE = c(a, b)) # efficacy look
if (postp >= tU) {
- xU <- x
postU <- postp
- break # needed ?
+ xU <- x
+ break
}
}
# calculate lower CI at boundaries
diff --git a/examples/boundsPostprob.R b/examples/boundsPostprob.R
index 97491a9f..f4377968 100644
--- a/examples/boundsPostprob.R
+++ b/examples/boundsPostprob.R
@@ -1,10 +1,8 @@
-## 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:
+# 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:
boundsPostprob(
- nvec = c(10, 20, 30, 40), p0 = 0.20,
- tL = 0.10, tU = 0.90, a = 1, b = 1
+ nvec = c(10, 20, 30, 40), p0 = 0.20, p1 = 0.2,
+ tL = 0.60, tU = 0.60, 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.
diff --git a/man/boundsPostprob.Rd b/man/boundsPostprob.Rd
index 860636e2..b405a75c 100644
--- a/man/boundsPostprob.Rd
+++ b/man/boundsPostprob.Rd
@@ -7,7 +7,7 @@
boundsPostprob(nvec, p0, p1 = p0, tL, tU, a, b)
}
\arguments{
-\item{nvec}{(\code{numeric}):\cr a vector of number of patients in each look.}
+\item{nvec}{(\code{numeric}):\cr A vector of number of patients in each look.}
\item{p0}{(\code{number}):\cr lower Futility threshold of response rate.}
@@ -18,16 +18,6 @@ boundsPostprob(nvec, p0, p1 = p0, tL, tU, a, b)
\item{tU}{(\code{number}):\cr posterior probability threshold for being above \code{p1}.}
}
\value{
-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).
-
A matrix for each same size in \code{nvec}. For each sample size, the following is returned:
\itemize{
\item \code{xL} : the maximum number of responses that meet the futility.
@@ -45,23 +35,19 @@ binomial test.
}
\description{
This function is used to identify the efficacy and futility
-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
+boundaries based on the following rules:
}
\details{
-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
+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
}
\examples{
-## 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:
+# 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:
boundsPostprob(
- nvec = c(10, 20, 30, 40), p0 = 0.20,
- tL = 0.10, tU = 0.90, a = 1, b = 1
+ nvec = c(10, 20, 30, 40), p0 = 0.20, p1 = 0.2,
+ tL = 0.60, tU = 0.60, 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.
}
diff --git a/man/ocPredprob.Rd b/man/ocPredprob.Rd
index 25c8d9e2..fa79aa7c 100644
--- a/man/ocPredprob.Rd
+++ b/man/ocPredprob.Rd
@@ -117,3 +117,124 @@ The criteria for Decision 2 for Futility looks are :
}
}
}
+\examples{
+# 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
+# - 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.
+
+# Decision 1 with no wiggle.
+set.seed(20)
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.4,
+ p0 = 0.25,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 100,
+ wiggle = FALSE,
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with wiggle.
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.4,
+ p0 = 0.25,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = c(10, 20),
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with separate Futility and Efficacy looks at interim and final without wiggle.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.4,
+ p0 = 0.25,
+ p1 = 0.2,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = FALSE,
+ nnF = c(10, 15, 20),
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with separate Futility and Efficacy looks at interim and final with wiggle.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.4,
+ p0 = 0.25,
+ p1 = 0.2,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = c(10, 15, 20),
+ decision1 = TRUE
+)
+result$oc
+
+# Here we illustrate an example for Decision 2 with the following assumptions :
+# True response rate or truep of the treatment group = 60\%
+# The following are the Final Stop rules respectively :
+# - Final look for Efficacy: Pr( response rate > 25\% ) > 60\% or P(response rate > p0) > tT
+# - Final look for Futility: Pr( response rate < 25\% ) < 60\% or P(response rate < p1) > tF
+# - Interim look for Efficacy: Pr( success at final ) > 80\% or P(success at final) > phiU
+# - Interim look for Futility: Pr( failure at final ) > 80\% or P(failure at final) > phiFu
+# We assume a prior of treatment arm parE = Beta(1,1), unless otherwise indicated.
+
+# Decision 2 without wiggle.
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.6,
+ p0 = 0.25,
+ p1 = 0.25,
+ tT = 0.6,
+ tF = 0.6,
+ phiU = 0.8,
+ phiFu = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = FALSE,
+ nnF = c(10, 20),
+ decision1 = FALSE
+)
+result$oc
+
+# Decision 2 with wiggle and with Futility only at final with non-uniform beta prior parE.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.6,
+ p0 = 0.25,
+ p1 = 0.25,
+ tT = 0.6,
+ tF = 0.6,
+ phiL = 0.8,
+ phiU = 0.8,
+ parE = c(11, 19),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = 30,
+ decision1 = FALSE
+)
+result$oc
+}
diff --git a/tests/testthat/test-boundsPostProb.R b/tests/testthat/test-boundsPostProb.R
index 0ae6eadf..ccbc3458 100644
--- a/tests/testthat/test-boundsPostProb.R
+++ b/tests/testthat/test-boundsPostProb.R
@@ -4,25 +4,30 @@ test_that("h_decision_one_RctPredProbDist gives correct result and list when rel
nvec = c(10, 20, 30, 40),
p0 = 0.2,
p1 = 0.2,
- tL = 0.10,
- tU = 0.90,
+ tL = 0.60,
+ tU = 0.60,
a = 1,
b = 1
)
expected <- data.frame(
list(
nvec = c(10, 20, 30, 40),
- xL = c(0, 1, 2, 4),
- pL = c(0, 0.05, 0.0667, 0.1),
- postL = c(0.0859, 0.0576, 0.0374, 0.0664),
- UciL = c(0.2589, 0.2161, 0.1953, 0.2144),
- xU = c(4, 7, 9, 12),
- pU = c(0.4, 0.35, 0.3, 0.3),
- postU = c(0.9496, 0.9569, 0.9254, 0.9479),
- LciU = c(0.15, 0.1773, 0.1663, 0.1831)
+ xL = c(1, 3, 5, 6),
+ pL = c(0.1, 0.15, 0.1667, 0.15),
+ postL = c(0.6779, 0.6296, 0.6069, 0.739),
+ UciL = c(0.3942, 0.3437, 0.319, 0.2747),
+ xU = c(2, 5, 7, 9),
+ pU = c(0.2, 0.25, 0.2333, 0.225),
+ postU = c(0.6174, 0.7693, 0.73, 0.704),
+ LciU = c(0.0368, 0.1041, 0.115, 0.1227)
)
)
- expect_equal(result$xL, c(0, 1, 2, 4))
- expect_equal(result$pL, c(0.0000, 0.0500, 0.0667, 0.1000))
- expect_equal(result$UciL, c(0.2589, 0.2161, 0.1953, 0.2144))
+ expect_equal(result$xL, c(1, 3, 5, 6))
+ expect_equal(result$pL, c(0.1, 0.15, 0.1667, 0.15))
+ expect_equal(result$postL, c(0.6779, 0.6296, 0.6069, 0.739))
+ expect_equal(result$UciL, c(0.3942, 0.3437, 0.319, 0.2747))
+ expect_equal(result$xU, c(2, 5, 7, 9))
+ expect_equal(result$pU, c(0.2, 0.25, 0.2333, 0.225))
+ expect_equal(result$postU, c(0.6174, 0.7693, 0.73, 0.704))
+ expect_equal(result$LciU, c(0.0368, 0.1041, 0.115, 0.1227))
})
From 51db59a10385b06a25807d86c7984374f14a9d28 Mon Sep 17 00:00:00 2001
From: "27856297+dependabot-preview[bot]@users.noreply.github.com"
<27856297+dependabot-preview[bot]@users.noreply.github.com>
Date: Sun, 26 May 2024 16:10:51 +0000
Subject: [PATCH 08/36] [skip roxygen] [skip vbump] Roxygen Man Pages Auto
Update
---
man/ocPredprob.Rd | 121 ----------------------------------------------
1 file changed, 121 deletions(-)
diff --git a/man/ocPredprob.Rd b/man/ocPredprob.Rd
index fa79aa7c..25c8d9e2 100644
--- a/man/ocPredprob.Rd
+++ b/man/ocPredprob.Rd
@@ -117,124 +117,3 @@ The criteria for Decision 2 for Futility looks are :
}
}
}
-\examples{
-# 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
-# - 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.
-
-# Decision 1 with no wiggle.
-set.seed(20)
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.4,
- p0 = 0.25,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 100,
- wiggle = FALSE,
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with wiggle.
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.4,
- p0 = 0.25,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = TRUE,
- nnF = c(10, 20),
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with separate Futility and Efficacy looks at interim and final without wiggle.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.4,
- p0 = 0.25,
- p1 = 0.2,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = FALSE,
- nnF = c(10, 15, 20),
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with separate Futility and Efficacy looks at interim and final with wiggle.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.4,
- p0 = 0.25,
- p1 = 0.2,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = TRUE,
- nnF = c(10, 15, 20),
- decision1 = TRUE
-)
-result$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
-# - 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.
-
-# Decision 2 without wiggle.
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.6,
- p0 = 0.25,
- p1 = 0.25,
- tT = 0.6,
- tF = 0.6,
- phiU = 0.8,
- phiFu = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = FALSE,
- nnF = c(10, 20),
- decision1 = FALSE
-)
-result$oc
-
-# Decision 2 with wiggle and with Futility only at final with non-uniform beta prior parE.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.6,
- p0 = 0.25,
- p1 = 0.25,
- tT = 0.6,
- tF = 0.6,
- phiL = 0.8,
- phiU = 0.8,
- parE = c(11, 19),
- sim = 50,
- wiggle = TRUE,
- nnF = 30,
- decision1 = FALSE
-)
-result$oc
-}
From df9a7cacba18258425b9e91e57af6ef286e051f3 Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Sun, 26 May 2024 18:13:31 +0200
Subject: [PATCH 09/36] clean
---
R/boundsPostprob.R | 1 -
man/boundsPostprob.Rd | 2 --
tests/testthat/test-boundsPostProb.R | 2 +-
3 files changed, 1 insertion(+), 4 deletions(-)
diff --git a/R/boundsPostprob.R b/R/boundsPostprob.R
index f7b5415d..617d3095 100644
--- a/R/boundsPostprob.R
+++ b/R/boundsPostprob.R
@@ -2,7 +2,6 @@
#'
#' 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
#'
diff --git a/man/boundsPostprob.Rd b/man/boundsPostprob.Rd
index b405a75c..4488c20d 100644
--- a/man/boundsPostprob.Rd
+++ b/man/boundsPostprob.Rd
@@ -36,8 +36,6 @@ binomial test.
\description{
This function is used to identify the efficacy and futility
boundaries based on the following rules:
-}
-\details{
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
}
diff --git a/tests/testthat/test-boundsPostProb.R b/tests/testthat/test-boundsPostProb.R
index ccbc3458..cbc28b7b 100644
--- a/tests/testthat/test-boundsPostProb.R
+++ b/tests/testthat/test-boundsPostProb.R
@@ -1,5 +1,5 @@
# boundsPostProb ----
-test_that("h_decision_one_RctPredProbDist gives correct result and list when relativeDelta = TRUE", {
+test_that("boundsPostProb gives correct result and list", {
result <- boundsPostprob(
nvec = c(10, 20, 30, 40),
p0 = 0.2,
From bd327cc140ad1750c5edd26c1a8915c10edde5f7 Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Sun, 26 May 2024 18:14:56 +0200
Subject: [PATCH 10/36] NAMESPACE stuff
---
NAMESPACE | 1 -
man/ocPredprob.Rd | 121 ++++++++++++++++++++++++++++++++++++++++++++++
2 files changed, 121 insertions(+), 1 deletion(-)
diff --git a/NAMESPACE b/NAMESPACE
index 3af942e9..9b572e48 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -6,7 +6,6 @@ export(dbetaMix)
export(dbetabinom)
export(dbetabinomMix)
export(dbetadiff)
-export(logit)
export(myPlot)
export(myPlotDiff)
export(oc2)
diff --git a/man/ocPredprob.Rd b/man/ocPredprob.Rd
index 25c8d9e2..fa79aa7c 100644
--- a/man/ocPredprob.Rd
+++ b/man/ocPredprob.Rd
@@ -117,3 +117,124 @@ The criteria for Decision 2 for Futility looks are :
}
}
}
+\examples{
+# 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
+# - 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.
+
+# Decision 1 with no wiggle.
+set.seed(20)
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.4,
+ p0 = 0.25,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 100,
+ wiggle = FALSE,
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with wiggle.
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.4,
+ p0 = 0.25,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = c(10, 20),
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with separate Futility and Efficacy looks at interim and final without wiggle.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.4,
+ p0 = 0.25,
+ p1 = 0.2,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = FALSE,
+ nnF = c(10, 15, 20),
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with separate Futility and Efficacy looks at interim and final with wiggle.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.4,
+ p0 = 0.25,
+ p1 = 0.2,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = c(10, 15, 20),
+ decision1 = TRUE
+)
+result$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
+# - 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.
+
+# Decision 2 without wiggle.
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.6,
+ p0 = 0.25,
+ p1 = 0.25,
+ tT = 0.6,
+ tF = 0.6,
+ phiU = 0.8,
+ phiFu = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = FALSE,
+ nnF = c(10, 20),
+ decision1 = FALSE
+)
+result$oc
+
+# Decision 2 with wiggle and with Futility only at final with non-uniform beta prior parE.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.6,
+ p0 = 0.25,
+ p1 = 0.25,
+ tT = 0.6,
+ tF = 0.6,
+ phiL = 0.8,
+ phiU = 0.8,
+ parE = c(11, 19),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = 30,
+ decision1 = FALSE
+)
+result$oc
+}
From e25e4fd3df8e84bfc647c591ff9cbe9237a5dd7f Mon Sep 17 00:00:00 2001
From: "27856297+dependabot-preview[bot]@users.noreply.github.com"
<27856297+dependabot-preview[bot]@users.noreply.github.com>
Date: Sun, 26 May 2024 16:17:30 +0000
Subject: [PATCH 11/36] [skip roxygen] [skip vbump] Roxygen Man Pages Auto
Update
---
man/ocPredprob.Rd | 121 ----------------------------------------------
1 file changed, 121 deletions(-)
diff --git a/man/ocPredprob.Rd b/man/ocPredprob.Rd
index fa79aa7c..25c8d9e2 100644
--- a/man/ocPredprob.Rd
+++ b/man/ocPredprob.Rd
@@ -117,124 +117,3 @@ The criteria for Decision 2 for Futility looks are :
}
}
}
-\examples{
-# 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
-# - 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.
-
-# Decision 1 with no wiggle.
-set.seed(20)
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.4,
- p0 = 0.25,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 100,
- wiggle = FALSE,
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with wiggle.
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.4,
- p0 = 0.25,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = TRUE,
- nnF = c(10, 20),
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with separate Futility and Efficacy looks at interim and final without wiggle.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.4,
- p0 = 0.25,
- p1 = 0.2,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = FALSE,
- nnF = c(10, 15, 20),
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with separate Futility and Efficacy looks at interim and final with wiggle.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.4,
- p0 = 0.25,
- p1 = 0.2,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = TRUE,
- nnF = c(10, 15, 20),
- decision1 = TRUE
-)
-result$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
-# - 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.
-
-# Decision 2 without wiggle.
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.6,
- p0 = 0.25,
- p1 = 0.25,
- tT = 0.6,
- tF = 0.6,
- phiU = 0.8,
- phiFu = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = FALSE,
- nnF = c(10, 20),
- decision1 = FALSE
-)
-result$oc
-
-# Decision 2 with wiggle and with Futility only at final with non-uniform beta prior parE.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.6,
- p0 = 0.25,
- p1 = 0.25,
- tT = 0.6,
- tF = 0.6,
- phiL = 0.8,
- phiU = 0.8,
- parE = c(11, 19),
- sim = 50,
- wiggle = TRUE,
- nnF = 30,
- decision1 = FALSE
-)
-result$oc
-}
From c6ec5cc4851db079b1edb3a8e15fdaa3ebb10098 Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Mon, 27 May 2024 12:08:37 +0200
Subject: [PATCH 12/36] empty
From fe22df674048ba24a13c9d07195b58dfdec8e804 Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Mon, 27 May 2024 14:14:43 +0200
Subject: [PATCH 13/36] add rmarkdown to DESCRIPTIOPM
---
DESCRIPTION | 1 +
1 file changed, 1 insertion(+)
diff --git a/DESCRIPTION b/DESCRIPTION
index bfdb50be..6fd25763 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -22,6 +22,7 @@ BugReports: https://github.com/genentech/phase1b/issues
Depends:
R (>= 3.6)
Suggests:
+ rmarkdown,
knitr,
officer,
shiny,
From 95a6080e5eb1a143a6e57db3d2042451c4fd6e40 Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Mon, 27 May 2024 15:27:51 +0200
Subject: [PATCH 14/36] clean
---
DESCRIPTION | 2 +
R/boundsPostprob.R | 16 ++--
man/boundsPostprob.Rd | 4 +-
man/ocPredprob.Rd | 121 +++++++++++++++++++++++++++
tests/testthat/test-boundsPostProb.R | 8 +-
5 files changed, 137 insertions(+), 14 deletions(-)
diff --git a/DESCRIPTION b/DESCRIPTION
index a7e3765b..7f3faddd 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -22,6 +22,8 @@ BugReports: https://github.com/genentech/phase1b/issues
Depends:
R (>= 3.6)
Suggests:
+ rmarkdown,
+ bookdown,
knitr,
officer,
shiny,
diff --git a/R/boundsPostprob.R b/R/boundsPostprob.R
index 617d3095..b50fee96 100644
--- a/R/boundsPostprob.R
+++ b/R/boundsPostprob.R
@@ -14,12 +14,12 @@
#' threshold
#' - `pL` : response rate 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 `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`.
-#' - `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 `pU` based on exact
#' binomial test.
#'
#' @example examples/boundsPostprob.R
@@ -31,8 +31,8 @@ boundsPostprob <- function(nvec, p0, p1 = p0, tL, tU, a, b) {
"xU", "pU", "postU"
))
znames <- c(
- "xL", "pL", "postL", "UciL",
- "xU", "pU", "postU", "LciU"
+ "xL", "pL", "postL", "pL_upper_ci",
+ "xU", "pU", "postU", "pU_lower_ci"
)
z <- matrix(NA, length(nvec), length(znames))
dimnames(z) <- list(nvec, znames)
@@ -56,17 +56,17 @@ boundsPostprob <- function(nvec, p0, p1 = p0, tL, tU, 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)
+ 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,
- UciL,
+ pL_upper_ci,
xU,
xU / n,
postU,
- LciU
+ pU_lower_ci
)
}
return(round(data.frame(nvec, z), 4))
diff --git a/man/boundsPostprob.Rd b/man/boundsPostprob.Rd
index 4488c20d..4394ef94 100644
--- a/man/boundsPostprob.Rd
+++ b/man/boundsPostprob.Rd
@@ -24,12 +24,12 @@ A matrix for each same size in \code{nvec}. For each sample size, the following
threshold
\item \code{pL} : response rate corresponding to \code{xL}.
\item \code{postL}: posterior probability corresponding to \code{xL}.
-\item \code{Ucil} : upper bound of one sided 95\% CI for the response rate based on an
+\item \code{pL_upper_ci} : upper bound of one sided 95\% CI for the response rate \code{pL} based on an
exact binomial test.
\item \code{xU} : the minimal number of responses that meet the efficacy threshold.
\item \code{pU} : response rate corresponding to \code{xU}.
\item \code{postU} : posterior probability corresponding to \code{xU}.
-\item \code{LciU} : lower bound of one sided 95\% CI for the response rate based on exact
+\item \code{pU_lower_ci} : lower bound of one sided 95\% CI for the response rate \code{pU} based on exact
binomial test.
}
}
diff --git a/man/ocPredprob.Rd b/man/ocPredprob.Rd
index 25c8d9e2..fa79aa7c 100644
--- a/man/ocPredprob.Rd
+++ b/man/ocPredprob.Rd
@@ -117,3 +117,124 @@ The criteria for Decision 2 for Futility looks are :
}
}
}
+\examples{
+# 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
+# - 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.
+
+# Decision 1 with no wiggle.
+set.seed(20)
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.4,
+ p0 = 0.25,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 100,
+ wiggle = FALSE,
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with wiggle.
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.4,
+ p0 = 0.25,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = c(10, 20),
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with separate Futility and Efficacy looks at interim and final without wiggle.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.4,
+ p0 = 0.25,
+ p1 = 0.2,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = FALSE,
+ nnF = c(10, 15, 20),
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with separate Futility and Efficacy looks at interim and final with wiggle.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.4,
+ p0 = 0.25,
+ p1 = 0.2,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = c(10, 15, 20),
+ decision1 = TRUE
+)
+result$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
+# - 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.
+
+# Decision 2 without wiggle.
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.6,
+ p0 = 0.25,
+ p1 = 0.25,
+ tT = 0.6,
+ tF = 0.6,
+ phiU = 0.8,
+ phiFu = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = FALSE,
+ nnF = c(10, 20),
+ decision1 = FALSE
+)
+result$oc
+
+# Decision 2 with wiggle and with Futility only at final with non-uniform beta prior parE.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.6,
+ p0 = 0.25,
+ p1 = 0.25,
+ tT = 0.6,
+ tF = 0.6,
+ phiL = 0.8,
+ phiU = 0.8,
+ parE = c(11, 19),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = 30,
+ decision1 = FALSE
+)
+result$oc
+}
diff --git a/tests/testthat/test-boundsPostProb.R b/tests/testthat/test-boundsPostProb.R
index cbc28b7b..3d74d3bc 100644
--- a/tests/testthat/test-boundsPostProb.R
+++ b/tests/testthat/test-boundsPostProb.R
@@ -15,19 +15,19 @@ test_that("boundsPostProb gives correct result and list", {
xL = c(1, 3, 5, 6),
pL = c(0.1, 0.15, 0.1667, 0.15),
postL = c(0.6779, 0.6296, 0.6069, 0.739),
- UciL = c(0.3942, 0.3437, 0.319, 0.2747),
+ pL_upper_ci = c(0.3942, 0.3437, 0.319, 0.2747),
xU = c(2, 5, 7, 9),
pU = c(0.2, 0.25, 0.2333, 0.225),
postU = c(0.6174, 0.7693, 0.73, 0.704),
- LciU = c(0.0368, 0.1041, 0.115, 0.1227)
+ pU_lower_ci = c(0.0368, 0.1041, 0.115, 0.1227)
)
)
expect_equal(result$xL, c(1, 3, 5, 6))
expect_equal(result$pL, c(0.1, 0.15, 0.1667, 0.15))
expect_equal(result$postL, c(0.6779, 0.6296, 0.6069, 0.739))
- expect_equal(result$UciL, c(0.3942, 0.3437, 0.319, 0.2747))
+ expect_equal(result$pL_upper_ci, c(0.3942, 0.3437, 0.319, 0.2747))
expect_equal(result$xU, c(2, 5, 7, 9))
expect_equal(result$pU, c(0.2, 0.25, 0.2333, 0.225))
expect_equal(result$postU, c(0.6174, 0.7693, 0.73, 0.704))
- expect_equal(result$LciU, c(0.0368, 0.1041, 0.115, 0.1227))
+ expect_equal(result$pU_lower_ci, c(0.0368, 0.1041, 0.115, 0.1227))
})
From 670558da97cd2b4da3246b84013dd105cf89b64f Mon Sep 17 00:00:00 2001
From: "27856297+dependabot-preview[bot]@users.noreply.github.com"
<27856297+dependabot-preview[bot]@users.noreply.github.com>
Date: Mon, 27 May 2024 13:31:18 +0000
Subject: [PATCH 15/36] [skip roxygen] [skip vbump] Roxygen Man Pages Auto
Update
---
man/ocPredprob.Rd | 121 ----------------------------------------------
1 file changed, 121 deletions(-)
diff --git a/man/ocPredprob.Rd b/man/ocPredprob.Rd
index fa79aa7c..25c8d9e2 100644
--- a/man/ocPredprob.Rd
+++ b/man/ocPredprob.Rd
@@ -117,124 +117,3 @@ The criteria for Decision 2 for Futility looks are :
}
}
}
-\examples{
-# 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
-# - 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.
-
-# Decision 1 with no wiggle.
-set.seed(20)
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.4,
- p0 = 0.25,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 100,
- wiggle = FALSE,
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with wiggle.
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.4,
- p0 = 0.25,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = TRUE,
- nnF = c(10, 20),
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with separate Futility and Efficacy looks at interim and final without wiggle.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.4,
- p0 = 0.25,
- p1 = 0.2,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = FALSE,
- nnF = c(10, 15, 20),
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with separate Futility and Efficacy looks at interim and final with wiggle.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.4,
- p0 = 0.25,
- p1 = 0.2,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = TRUE,
- nnF = c(10, 15, 20),
- decision1 = TRUE
-)
-result$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
-# - 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.
-
-# Decision 2 without wiggle.
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.6,
- p0 = 0.25,
- p1 = 0.25,
- tT = 0.6,
- tF = 0.6,
- phiU = 0.8,
- phiFu = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = FALSE,
- nnF = c(10, 20),
- decision1 = FALSE
-)
-result$oc
-
-# Decision 2 with wiggle and with Futility only at final with non-uniform beta prior parE.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.6,
- p0 = 0.25,
- p1 = 0.25,
- tT = 0.6,
- tF = 0.6,
- phiL = 0.8,
- phiU = 0.8,
- parE = c(11, 19),
- sim = 50,
- wiggle = TRUE,
- nnF = 30,
- decision1 = FALSE
-)
-result$oc
-}
From a29fd0fd1921d2e89390f3e1dd3293cf7d62d1f6 Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Sun, 2 Jun 2024 19:37:45 +0200
Subject: [PATCH 16/36] should pass CMD checks
From fe3f37fac2d62b821404a5ebbf9b93b57cccb041 Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Mon, 3 Jun 2024 08:43:45 +0200
Subject: [PATCH 17/36] see if CMD checks pass now
From d4759efe3f9f5940e3025052ba573127ed637a6b Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Tue, 11 Jun 2024 10:49:30 +0200
Subject: [PATCH 18/36] clean
---
NAMESPACE | 2 +-
R/plotBeta.R | 36 +++++++------
inst/WORDLIST | 1 +
man/myPlotDiff.Rd | 33 ------------
man/ocPredprob.Rd | 121 ++++++++++++++++++++++++++++++++++++++++++++
man/plotBetaDist.Rd | 33 ++++++++++++
6 files changed, 177 insertions(+), 49 deletions(-)
delete mode 100644 man/myPlotDiff.Rd
create mode 100644 man/plotBetaDist.Rd
diff --git a/NAMESPACE b/NAMESPACE
index 9b572e48..7221e4b2 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -7,7 +7,6 @@ export(dbetabinom)
export(dbetabinomMix)
export(dbetadiff)
export(myPlot)
-export(myPlotDiff)
export(oc2)
export(oc3)
export(ocPostprob)
@@ -18,6 +17,7 @@ export(ocRctPostprobDist)
export(ocRctPredprobDist)
export(pbetaMix)
export(pbetadiff)
+export(plotBetaDist)
export(plotBounds)
export(plotDecision)
export(plotOc)
diff --git a/R/plotBeta.R b/R/plotBeta.R
index d4ebf5a3..00ae90f9 100644
--- a/R/plotBeta.R
+++ b/R/plotBeta.R
@@ -41,14 +41,20 @@ myPlot <- function(alpha, beta, ...) {
#'
#' This function will plot the PDF of a diffience between two Beta distributions
#'
-#' @param parY non-negative parameters of the treatment Beta distribution.
-#' @param parX non-negative parameters of the historical control Beta distribution
-#' @param cut_B a meaningful improvement threshold
-#' @param cut_W a poor improvement throshold
-#' @param shade paint the two areas under the curve, default value=1 as "yes". other numbers stands for "no";
-#' @param note show values of the colored area, default value=1 as "yes". other numbers stands for "no"
-#' @param \dots additional arguments to \code{plot}
-#' @return nothing, only produces the plot as side effect
+#' @typed parY : numeric
+#' non-negative parameters of the treatment Beta distribution.
+#' @typed parX : numeric
+#' non-negative parameters of the historical control Beta distribution
+#' @typed cut_B : number
+#' a meaningful improvement threshold, the lower boundary of a meaningfully improvement in response rate
+#' @typed cut_W : number
+#' a poor improvement throshold, the upper boundary of a meaningfully poor improvement in response rate
+#' @typed shade :
+#' paint the two areas under the curve, default value=1 as "yes". other numbers stands for "no";
+#' @typed note : number
+#' show values of the colored area, default value=1 as "yes". other numbers stands for "no"
+#' @typed \dots additional arguments to \code{plot}
+#' @return a ggplot object
#'
#' @example examples/myPlotDiff.R
#'
@@ -57,13 +63,13 @@ myPlot <- function(alpha, beta, ...) {
#'
#' @export
#' @keywords graphics
-myPlotDiff <- function(parY, # parameters of phase Ib trial;
- parX, # parameters of HC;
- cut_B = 0.20, # a meaningful improvement threshold;
- cut_W = 0.1, # a poor improvement threshold;
- shade = 1, # paint the two areas under the curve, default: yes. other numbers stands for "no";
- note = 1, # show values of the colored area, default: yes. other numbers stands for "no";
- ...) {
+plotBetaDist <- function(parY, # parameters of phase Ib trial;
+ parX, # parameters of HC;
+ cut_B = 0.20, # a meaningful improvement threshold;
+ cut_W = 0.1, # a poor improvement threshold;
+ shade = 1, # paint the two areas under the curve, default: yes. other numbers stands for "no";
+ note = 1, # show values of the colored area, default: yes. other numbers stands for "no";
+ ...) {
if (note == 1) {
graphics::par(mar = c(5, 15, 1, 15) + .1)
} else {
diff --git a/inst/WORDLIST b/inst/WORDLIST
index f6be2e38..178e3901 100644
--- a/inst/WORDLIST
+++ b/inst/WORDLIST
@@ -94,6 +94,7 @@ frac
funder
generalizable
geq
+ggplot
grayzone
grey
hier
diff --git a/man/myPlotDiff.Rd b/man/myPlotDiff.Rd
deleted file mode 100644
index 6be1ecbf..00000000
--- a/man/myPlotDiff.Rd
+++ /dev/null
@@ -1,33 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/plotBeta.R
-\name{myPlotDiff}
-\alias{myPlotDiff}
-\title{Plot Diff Between two Beta distributions}
-\usage{
-myPlotDiff(parY, parX, cut_B = 0.2, cut_W = 0.1, shade = 1, note = 1, ...)
-}
-\arguments{
-\item{parY}{non-negative parameters of the treatment Beta distribution.}
-
-\item{parX}{non-negative parameters of the historical control Beta distribution}
-
-\item{cut_B}{a meaningful improvement threshold}
-
-\item{cut_W}{a poor improvement throshold}
-
-\item{shade}{paint the two areas under the curve, default value=1 as "yes". other numbers stands for "no";}
-
-\item{note}{show values of the colored area, default value=1 as "yes". other numbers stands for "no"}
-
-\item{\dots}{additional arguments to \code{plot}}
-}
-\value{
-nothing, only produces the plot as side effect
-}
-\description{
-This function will plot the PDF of a diffience between two Beta distributions
-}
-\examples{
-myPlotDiff(c(5, 10), c(2, 5), 0.2, 0.05, 1, 0)
-}
-\keyword{graphics}
diff --git a/man/ocPredprob.Rd b/man/ocPredprob.Rd
index 25c8d9e2..63e17a09 100644
--- a/man/ocPredprob.Rd
+++ b/man/ocPredprob.Rd
@@ -117,3 +117,124 @@ The criteria for Decision 2 for Futility looks are :
}
}
}
+\examples{
+# 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
+# - 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.
+
+# Decision 1 with no wiggle.
+set.seed(20)
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.4,
+ p0 = 0.25,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = FALSE,
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with wiggle.
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.4,
+ p0 = 0.25,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = c(10, 20),
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with separate Futility and Efficacy looks at interim and final without wiggle.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.4,
+ p0 = 0.25,
+ p1 = 0.2,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = FALSE,
+ nnF = c(10, 15, 20),
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with separate Futility and Efficacy looks at interim and final with wiggle.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.4,
+ p0 = 0.25,
+ p1 = 0.2,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = c(10, 15, 20),
+ decision1 = TRUE
+)
+result$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
+# - 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.
+
+# Decision 2 without wiggle.
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.6,
+ p0 = 0.25,
+ p1 = 0.25,
+ tT = 0.6,
+ tF = 0.6,
+ phiU = 0.8,
+ phiFu = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = FALSE,
+ nnF = c(10, 20),
+ decision1 = FALSE
+)
+result$oc
+
+# Decision 2 with wiggle and with Futility only at final with non-uniform beta prior parE.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.6,
+ p0 = 0.25,
+ p1 = 0.25,
+ tT = 0.6,
+ tF = 0.6,
+ phiL = 0.8,
+ phiU = 0.8,
+ parE = c(11, 19),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = 30,
+ decision1 = FALSE
+)
+result$oc
+}
diff --git a/man/plotBetaDist.Rd b/man/plotBetaDist.Rd
new file mode 100644
index 00000000..1b9c8d5e
--- /dev/null
+++ b/man/plotBetaDist.Rd
@@ -0,0 +1,33 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/plotBeta.R
+\name{plotBetaDist}
+\alias{plotBetaDist}
+\title{Plot Diff Between two Beta distributions}
+\usage{
+plotBetaDist(parY, parX, cut_B = 0.2, cut_W = 0.1, shade = 1, note = 1, ...)
+}
+\arguments{
+\item{parY}{(\code{numeric}):\cr non-negative parameters of the treatment Beta distribution.}
+
+\item{parX}{(\code{numeric}):\cr non-negative parameters of the historical control Beta distribution}
+
+\item{cut_B}{(\code{number}):\cr a meaningful improvement threshold, the lower boundary of a meaningfully improvement in response rate}
+
+\item{cut_W}{(\code{number}):\cr a poor improvement throshold, the upper boundary of a meaningfully poor improvement in response rate}
+
+\item{shade}{(``):\cr paint the two areas under the curve, default value=1 as "yes". other numbers stands for "no";}
+
+\item{note}{(\code{number}):\cr show values of the colored area, default value=1 as "yes". other numbers stands for "no"}
+
+\item{<parse error>}{(\if{html}{\out{<parse error>}}):\cr \if{html}{\out{<parse error>}}}
+}
+\value{
+a ggplot object
+}
+\description{
+This function will plot the PDF of a diffience between two Beta distributions
+}
+\examples{
+myPlotDiff(c(5, 10), c(2, 5), 0.2, 0.05, 1, 0)
+}
+\keyword{graphics}
From 188027d4924193f240e01c642bc8c9126b64fedc Mon Sep 17 00:00:00 2001
From: "27856297+dependabot-preview[bot]@users.noreply.github.com"
<27856297+dependabot-preview[bot]@users.noreply.github.com>
Date: Tue, 11 Jun 2024 09:02:38 +0000
Subject: [PATCH 19/36] [skip roxygen] [skip vbump] Roxygen Man Pages Auto
Update
---
man/ocPredprob.Rd | 121 ----------------------------------------------
1 file changed, 121 deletions(-)
diff --git a/man/ocPredprob.Rd b/man/ocPredprob.Rd
index 63e17a09..25c8d9e2 100644
--- a/man/ocPredprob.Rd
+++ b/man/ocPredprob.Rd
@@ -117,124 +117,3 @@ The criteria for Decision 2 for Futility looks are :
}
}
}
-\examples{
-# 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
-# - 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.
-
-# Decision 1 with no wiggle.
-set.seed(20)
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.4,
- p0 = 0.25,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = FALSE,
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with wiggle.
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.4,
- p0 = 0.25,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = TRUE,
- nnF = c(10, 20),
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with separate Futility and Efficacy looks at interim and final without wiggle.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.4,
- p0 = 0.25,
- p1 = 0.2,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = FALSE,
- nnF = c(10, 15, 20),
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with separate Futility and Efficacy looks at interim and final with wiggle.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.4,
- p0 = 0.25,
- p1 = 0.2,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = TRUE,
- nnF = c(10, 15, 20),
- decision1 = TRUE
-)
-result$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
-# - 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.
-
-# Decision 2 without wiggle.
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.6,
- p0 = 0.25,
- p1 = 0.25,
- tT = 0.6,
- tF = 0.6,
- phiU = 0.8,
- phiFu = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = FALSE,
- nnF = c(10, 20),
- decision1 = FALSE
-)
-result$oc
-
-# Decision 2 with wiggle and with Futility only at final with non-uniform beta prior parE.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.6,
- p0 = 0.25,
- p1 = 0.25,
- tT = 0.6,
- tF = 0.6,
- phiL = 0.8,
- phiU = 0.8,
- parE = c(11, 19),
- sim = 50,
- wiggle = TRUE,
- nnF = 30,
- decision1 = FALSE
-)
-result$oc
-}
From 40b490e433e134b8d3f24f7f2803d9d170b5ec4f Mon Sep 17 00:00:00 2001
From: "27856297+dependabot-preview[bot]@users.noreply.github.com"
<27856297+dependabot-preview[bot]@users.noreply.github.com>
Date: Mon, 16 Sep 2024 12:43:30 +0000
Subject: [PATCH 20/36] [skip roxygen] [skip vbump] Roxygen Man Pages Auto
Update
---
man/ocPredprob.Rd | 121 ----------------------------------------------
1 file changed, 121 deletions(-)
diff --git a/man/ocPredprob.Rd b/man/ocPredprob.Rd
index 63e17a09..25c8d9e2 100644
--- a/man/ocPredprob.Rd
+++ b/man/ocPredprob.Rd
@@ -117,124 +117,3 @@ The criteria for Decision 2 for Futility looks are :
}
}
}
-\examples{
-# 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
-# - 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.
-
-# Decision 1 with no wiggle.
-set.seed(20)
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.4,
- p0 = 0.25,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = FALSE,
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with wiggle.
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.4,
- p0 = 0.25,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = TRUE,
- nnF = c(10, 20),
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with separate Futility and Efficacy looks at interim and final without wiggle.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.4,
- p0 = 0.25,
- p1 = 0.2,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = FALSE,
- nnF = c(10, 15, 20),
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with separate Futility and Efficacy looks at interim and final with wiggle.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.4,
- p0 = 0.25,
- p1 = 0.2,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = TRUE,
- nnF = c(10, 15, 20),
- decision1 = TRUE
-)
-result$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
-# - 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.
-
-# Decision 2 without wiggle.
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.6,
- p0 = 0.25,
- p1 = 0.25,
- tT = 0.6,
- tF = 0.6,
- phiU = 0.8,
- phiFu = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = FALSE,
- nnF = c(10, 20),
- decision1 = FALSE
-)
-result$oc
-
-# Decision 2 with wiggle and with Futility only at final with non-uniform beta prior parE.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.6,
- p0 = 0.25,
- p1 = 0.25,
- tT = 0.6,
- tF = 0.6,
- phiL = 0.8,
- phiU = 0.8,
- parE = c(11, 19),
- sim = 50,
- wiggle = TRUE,
- nnF = 30,
- decision1 = FALSE
-)
-result$oc
-}
From 97b06858f6cf0a644a8ee77248adf18d1f1aa18f Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Sun, 22 Sep 2024 20:37:02 +0200
Subject: [PATCH 21/36] just some first drafts
---
R/plotBeta.R | 157 +++++++++++++-------------------------------
examples/plotBeta.R | 13 ++++
man/ocPredprob.Rd | 121 ++++++++++++++++++++++++++++++++++
man/plotBeta.Rd | 15 ++++-
man/plotBetaDiff.Rd | 15 ++---
5 files changed, 201 insertions(+), 120 deletions(-)
diff --git a/R/plotBeta.R b/R/plotBeta.R
index e22d383c..8552e6ae 100644
--- a/R/plotBeta.R
+++ b/R/plotBeta.R
@@ -2,11 +2,11 @@
#'
#' This function will plot the PDF of a beta distribution
#'
+#' @inheritParams dbetabinom
#' @typed alpha : number
#' first parameter of the Beta distribution
#' @typed beta : number
#' second parameter of the Beta distribution
-#'
#' @return A beta distribution density plot
#'
#' @importFrom graphics axis
@@ -14,7 +14,7 @@
#' @example examples/plotBeta.R
#' @export
#' @keywords graphics
-plotBeta <- function(alpha, beta) {
+plotBeta <- function(alpha, beta, ...) {
x_support <- seq(from = 0, to = 1, length = 1000)
data <- data.frame(
grid = x_support,
@@ -57,123 +57,58 @@ plotBeta <- function(alpha, beta) {
#'
#' @export
#' @keywords graphics
-plotBetaDiff <- function(parY, # parameters of phase Ib trial;
- parX, # parameters of HC;
- cut_B = 0.20, # a meaningful improvement threshold;
- cut_W = 0.1, # a poor improvement threshold;
- shade = TRUE, # paint the two areas under the curve,
- # default: yes. other numbers stands for "no";
- note = TRUE, # show values of the colored area,
- # default: yes. other numbers stands for "no";
- ...) {
- if (note == 1) {
- graphics::par(mar = c(5, 15, 1, 15) + .1)
- } else {
- graphics::par(mar = c(5, 5, 1, 5) + .1)
- }
- grid <- seq(from = -0.5, to = 0.75, length = 1000)
- xticks <- seq(from = -1, to = 1, by = 0.25)
+plotBetaDiff <- function(parY, # parameters of experimental arm
+ parX, # parameters of control or SOC
+ Go_cut = 0.20, # a meaningful improvement threshold
+ Stop_cut = 0.1, # a poor improvement threshold
+ shade = TRUE, # paint the two areas under the curve
+ note = TRUE) { # show values of the colored area
+ diff <- seq(from = -1, to = 1, length = 1000)
+ data <- data.frame(
+ grid = diff,
+ xticks = seq(from = 0, to = 1, by = 0.25),
+ density = dbetadiff(z = diff, parY = parY, parX = parX)
+ )
+ data$Stop <- ifelse(diff > -1 & diff < Stop_cut, TRUE, FALSE)
+ data$Go <- ifelse(diff > Go_cut & diff < 1, TRUE, FALSE)
+ # shade the AUC for Go if difference was `lower` :
- graphics::plot(
- x = grid,
- y = dbetadiff(grid, parY = parY, parX = parX),
- ylab = "",
- xaxt = "n",
- yaxt = "n",
- type = "l",
- xaxs = "i",
- yaxs = "i",
- ...
+ Go_auc <- integrate(
+ f = dbetadiff,
+ parY = parY,
+ parX = parX,
+ lower = Go_cut, # Calculate probability of Go, if difference was at least `Go_cut`.
+ upper = 1
)
-
- graphics::axis(
- side = 1, at = xticks,
- labels =
- paste(ifelse(xticks >= 0, "+", ""),
- xticks * 100, "%",
- sep = ""
- )
+ Stop_auc <- integrate(
+ f = dbetadiff,
+ parY = parY,
+ parX = parX,
+ lower = -1,
+ upper = Stop_cut # Calculate probability of Stop, if difference was at most `Stop_cut`.
)
- ## now color the go / stop prob areas
+ Go_label <- paste("Probability of Go is", round(Go_auc$value * 100, digits = 2), "%")
+ Stop_label <- paste("Probability of Stop is", round(Stop_auc$value * 100, digits = 2), "%")
+ plot_title <- paste("According to Beta difference density", Go_label, "and", Stop_label)
- if (shade == 1) {
- ## first stop:
- stopGrid <- grid[grid <= cut_W]
- nStop <- length(stopGrid)
-
- graphics::polygon(
- x =
- c(
- stopGrid,
- rev(stopGrid)
- ),
- y =
- c(
- rep(0, nStop),
- dbetadiff(rev(stopGrid), parY = parY, parX = parX)
- ),
- col = "red"
- )
-
- A_value <- stats::integrate(
- f = dbetadiff,
- parY = parY,
- parX = parX,
- lower = -1,
- upper = cut_W
- )
- if (note == 1) {
- graphics::mtext(
- paste("Prob(diff< ", round(cut_W * 100), "%)=",
- sprintf("%1.2f%%", 100 * as.numeric(A_value$value)),
- sep = ""
- ),
- side = 2, line = 1, las = 1, cex = 1
- )
- }
-
- ## then go:
- goGrid <- grid[grid >= cut_B]
- nGo <- length(goGrid)
-
- graphics::polygon(
- x =
- c(
- goGrid,
- rev(goGrid)
- ),
- y =
- c(
- rep(0, nGo),
- dbetadiff(rev(goGrid), parY = parY, parX = parX)
- ),
- col = "green"
- )
+ pbetadiff_plot <- ggplot2::ggplot(data = data, mapping = aes(x = grid, y = density)) +
+ ggplot2::geom_line(colour = "#888888") +
+ xlab("Difference between treatment") +
+ ggplot2::ylab(quote(f(x))) +
+ ggplot2::ggtitle(plot_title)
- B_value <- stats::integrate(
- f = dbetadiff,
- parY = parY,
- parX = parX,
- lower = cut_B,
- upper = 1
- )
+ if (shade == TRUE) {
+ pbetadiff_plot +
+ ggplot2::geom_area(data = filter(data, Go == TRUE), fill = "#009E73") +
+ ggplot2::geom_area(data = filter(data, Stop == TRUE), fill = "#D55E00")
+ }
- if (note == 1) {
- graphics::mtext(
- paste(
- sprintf("%1.2f%%", 100 * as.numeric(B_value$value)),
- "=Prob(diff> ",
- round(cut_B * 100), "%)",
- sep = ""
- ),
- side = 4,
- line = 1,
- las = 1,
- cex = 1
- )
- }
+ if (note == TRUE) {
+ pbetadiff_plot +
+ ggplot2::annotate("text", x = -0.5, y = 4.25, label = Go_label, colour = "#009E73") +
+ ggplot2::annotate("text", x = -0.5, y = 4.75, label = Stop_label, colour = "#D55E00")
}
}
diff --git a/examples/plotBeta.R b/examples/plotBeta.R
index 6e84087b..9fd6a98c 100644
--- a/examples/plotBeta.R
+++ b/examples/plotBeta.R
@@ -1,2 +1,15 @@
+# plotBeta
plotBeta(alpha = 4, beta = 5)
plotBeta(alpha = 1, beta = 1)
+
+# plotBetDiff
+parX <- c(1, 52) # parameters of experimental arm
+parY <- c(5.5, 20.5) # parameters of control or SOC
+plotBetaDiff(
+ parY = parY,
+ parX = parX,
+ Go_cut = 0.3,
+ Stop_cut = 0.1,
+ shade = TRUE,
+ note = TRUE
+)
diff --git a/man/ocPredprob.Rd b/man/ocPredprob.Rd
index 25c8d9e2..63e17a09 100644
--- a/man/ocPredprob.Rd
+++ b/man/ocPredprob.Rd
@@ -117,3 +117,124 @@ The criteria for Decision 2 for Futility looks are :
}
}
}
+\examples{
+# 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
+# - 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.
+
+# Decision 1 with no wiggle.
+set.seed(20)
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.4,
+ p0 = 0.25,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = FALSE,
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with wiggle.
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.4,
+ p0 = 0.25,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = c(10, 20),
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with separate Futility and Efficacy looks at interim and final without wiggle.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.4,
+ p0 = 0.25,
+ p1 = 0.2,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = FALSE,
+ nnF = c(10, 15, 20),
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with separate Futility and Efficacy looks at interim and final with wiggle.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.4,
+ p0 = 0.25,
+ p1 = 0.2,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = c(10, 15, 20),
+ decision1 = TRUE
+)
+result$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
+# - 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.
+
+# Decision 2 without wiggle.
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.6,
+ p0 = 0.25,
+ p1 = 0.25,
+ tT = 0.6,
+ tF = 0.6,
+ phiU = 0.8,
+ phiFu = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = FALSE,
+ nnF = c(10, 20),
+ decision1 = FALSE
+)
+result$oc
+
+# Decision 2 with wiggle and with Futility only at final with non-uniform beta prior parE.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.6,
+ p0 = 0.25,
+ p1 = 0.25,
+ tT = 0.6,
+ tF = 0.6,
+ phiL = 0.8,
+ phiU = 0.8,
+ parE = c(11, 19),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = 30,
+ decision1 = FALSE
+)
+result$oc
+}
diff --git a/man/plotBeta.Rd b/man/plotBeta.Rd
index bf45b581..94577edd 100644
--- a/man/plotBeta.Rd
+++ b/man/plotBeta.Rd
@@ -4,7 +4,7 @@
\alias{plotBeta}
\title{Plot the Beta distribution}
\usage{
-plotBeta(alpha, beta)
+plotBeta(alpha, beta, ...)
}
\arguments{
\item{alpha}{(\code{number}):\cr first parameter of the Beta distribution}
@@ -18,7 +18,20 @@ A beta distribution density plot
This function will plot the PDF of a beta distribution
}
\examples{
+# plotBeta
plotBeta(alpha = 4, beta = 5)
plotBeta(alpha = 1, beta = 1)
+
+# plotBetDiff
+parX <- c(1, 52) # parameters of experimental arm
+parY <- c(5.5, 20.5) # parameters of control or SOC
+plotBetaDiff(
+ parY = parY,
+ parX = parX,
+ Go_cut = 0.3,
+ Stop_cut = 0.1,
+ shade = TRUE,
+ note = TRUE
+)
}
\keyword{graphics}
diff --git a/man/plotBetaDiff.Rd b/man/plotBetaDiff.Rd
index f8be7e5d..1651cf22 100644
--- a/man/plotBetaDiff.Rd
+++ b/man/plotBetaDiff.Rd
@@ -7,11 +7,10 @@
plotBetaDiff(
parY,
parX,
- cut_B = 0.2,
- cut_W = 0.1,
+ Go_cut = 0.2,
+ Stop_cut = 0.1,
shade = TRUE,
- note = TRUE,
- ...
+ note = TRUE
)
}
\arguments{
@@ -19,14 +18,14 @@ plotBetaDiff(
\item{parX}{(\code{numeric}):\cr non-negative parameters of the historical control Beta distribution}
-\item{cut_B}{(\code{number}):\cr a meaningful improvement threshold, the lower boundary of a meaningfully improvement in response rate}
-
-\item{cut_W}{(\code{number}):\cr a poor improvement threshold, the upper boundary of a meaningfully poor improvement in response rate}
-
\item{shade}{(\code{flag}):\cr paint the two areas under the curve, default value = TRUE}
\item{note}{(\code{flag}):\cr show values of the colored area, default value = TRUE}
+\item{cut_B}{(\code{number}):\cr a meaningful improvement threshold, the lower boundary of a meaningfully improvement in response rate}
+
+\item{cut_W}{(\code{number}):\cr a poor improvement threshold, the upper boundary of a meaningfully poor improvement in response rate}
+
\item{...}{(``):\cr additional arguments to \code{ggplot()}}
}
\value{
From be2e55b7fe19e0fe1becb198f4cbceefd9e73b83 Mon Sep 17 00:00:00 2001
From: "27856297+dependabot-preview[bot]@users.noreply.github.com"
<27856297+dependabot-preview[bot]@users.noreply.github.com>
Date: Sun, 22 Sep 2024 18:40:30 +0000
Subject: [PATCH 22/36] [skip roxygen] [skip vbump] Roxygen Man Pages Auto
Update
---
man/ocPredprob.Rd | 121 ----------------------------------------------
1 file changed, 121 deletions(-)
diff --git a/man/ocPredprob.Rd b/man/ocPredprob.Rd
index 63e17a09..25c8d9e2 100644
--- a/man/ocPredprob.Rd
+++ b/man/ocPredprob.Rd
@@ -117,124 +117,3 @@ The criteria for Decision 2 for Futility looks are :
}
}
}
-\examples{
-# 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
-# - 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.
-
-# Decision 1 with no wiggle.
-set.seed(20)
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.4,
- p0 = 0.25,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = FALSE,
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with wiggle.
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.4,
- p0 = 0.25,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = TRUE,
- nnF = c(10, 20),
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with separate Futility and Efficacy looks at interim and final without wiggle.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.4,
- p0 = 0.25,
- p1 = 0.2,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = FALSE,
- nnF = c(10, 15, 20),
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with separate Futility and Efficacy looks at interim and final with wiggle.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.4,
- p0 = 0.25,
- p1 = 0.2,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = TRUE,
- nnF = c(10, 15, 20),
- decision1 = TRUE
-)
-result$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
-# - 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.
-
-# Decision 2 without wiggle.
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.6,
- p0 = 0.25,
- p1 = 0.25,
- tT = 0.6,
- tF = 0.6,
- phiU = 0.8,
- phiFu = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = FALSE,
- nnF = c(10, 20),
- decision1 = FALSE
-)
-result$oc
-
-# Decision 2 with wiggle and with Futility only at final with non-uniform beta prior parE.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.6,
- p0 = 0.25,
- p1 = 0.25,
- tT = 0.6,
- tF = 0.6,
- phiL = 0.8,
- phiU = 0.8,
- parE = c(11, 19),
- sim = 50,
- wiggle = TRUE,
- nnF = 30,
- decision1 = FALSE
-)
-result$oc
-}
From 7e75e6801c6b429e6d1cfeb0a284e4c6a5850e92 Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Sun, 22 Sep 2024 20:42:35 +0200
Subject: [PATCH 23/36] exame file for plotBetaDiff.R
---
R/plotBeta.R | 3 ---
examples/myPlotDiff.R | 2 +-
examples/plotBeta.R | 12 ------------
examples/plotBetaDiff.R | 11 +++++++++++
man/plotBeta.Rd | 12 ------------
man/plotBetaDiff.Rd | 2 +-
6 files changed, 13 insertions(+), 29 deletions(-)
create mode 100644 examples/plotBetaDiff.R
diff --git a/R/plotBeta.R b/R/plotBeta.R
index 8552e6ae..88e2a5ea 100644
--- a/R/plotBeta.R
+++ b/R/plotBeta.R
@@ -69,12 +69,9 @@ plotBetaDiff <- function(parY, # parameters of experimental arm
xticks = seq(from = 0, to = 1, by = 0.25),
density = dbetadiff(z = diff, parY = parY, parX = parX)
)
-
data$Stop <- ifelse(diff > -1 & diff < Stop_cut, TRUE, FALSE)
data$Go <- ifelse(diff > Go_cut & diff < 1, TRUE, FALSE)
- # shade the AUC for Go if difference was `lower` :
-
Go_auc <- integrate(
f = dbetadiff,
parY = parY,
diff --git a/examples/myPlotDiff.R b/examples/myPlotDiff.R
index c9a4795e..991f9fc2 100644
--- a/examples/myPlotDiff.R
+++ b/examples/myPlotDiff.R
@@ -4,5 +4,5 @@ myPlotDiff(
cut_B = 0.2, # a meaningful improvement threshold
cut_W = 0.05, # a poor improvement threshold
shade = 1, # paint the two areas under the curve, default: yes. other numbers stands for "no";
- note = 0
+ note = 1
) # show values of the colored area, default: yes. other numbers stands for "no";
diff --git a/examples/plotBeta.R b/examples/plotBeta.R
index 9fd6a98c..712b5482 100644
--- a/examples/plotBeta.R
+++ b/examples/plotBeta.R
@@ -1,15 +1,3 @@
# plotBeta
plotBeta(alpha = 4, beta = 5)
plotBeta(alpha = 1, beta = 1)
-
-# plotBetDiff
-parX <- c(1, 52) # parameters of experimental arm
-parY <- c(5.5, 20.5) # parameters of control or SOC
-plotBetaDiff(
- parY = parY,
- parX = parX,
- Go_cut = 0.3,
- Stop_cut = 0.1,
- shade = TRUE,
- note = TRUE
-)
diff --git a/examples/plotBetaDiff.R b/examples/plotBetaDiff.R
new file mode 100644
index 00000000..0a22a11c
--- /dev/null
+++ b/examples/plotBetaDiff.R
@@ -0,0 +1,11 @@
+# plotBetDiff
+parX <- c(1, 52) # parameters of experimental arm
+parY <- c(5.5, 20.5) # parameters of control or SOC
+plotBetaDiff(
+ parY = parY,
+ parX = parX,
+ Go_cut = 0.3,
+ Stop_cut = 0.1,
+ shade = TRUE,
+ note = TRUE
+)
diff --git a/man/plotBeta.Rd b/man/plotBeta.Rd
index 94577edd..a17df369 100644
--- a/man/plotBeta.Rd
+++ b/man/plotBeta.Rd
@@ -21,17 +21,5 @@ This function will plot the PDF of a beta distribution
# plotBeta
plotBeta(alpha = 4, beta = 5)
plotBeta(alpha = 1, beta = 1)
-
-# plotBetDiff
-parX <- c(1, 52) # parameters of experimental arm
-parY <- c(5.5, 20.5) # parameters of control or SOC
-plotBetaDiff(
- parY = parY,
- parX = parX,
- Go_cut = 0.3,
- Stop_cut = 0.1,
- shade = TRUE,
- note = TRUE
-)
}
\keyword{graphics}
diff --git a/man/plotBetaDiff.Rd b/man/plotBetaDiff.Rd
index 1651cf22..6c7b9c4d 100644
--- a/man/plotBetaDiff.Rd
+++ b/man/plotBetaDiff.Rd
@@ -41,7 +41,7 @@ myPlotDiff(
cut_B = 0.2, # a meaningful improvement threshold
cut_W = 0.05, # a poor improvement threshold
shade = 1, # paint the two areas under the curve, default: yes. other numbers stands for "no";
- note = 0
+ note = 1
) # show values of the colored area, default: yes. other numbers stands for "no";
}
\keyword{graphics}
From ec5abb8d7548228ab1e30f069f0664fa362694e3 Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Mon, 27 May 2024 12:08:37 +0200
Subject: [PATCH 24/36] empty
From 2757aab2356968045c393c4ef935913772f7dc23 Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Sun, 2 Jun 2024 19:37:45 +0200
Subject: [PATCH 25/36] should pass CMD checks
From 93f463a9cee89eab60642bf79a616d58ebefc663 Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Sun, 22 Sep 2024 20:53:40 +0200
Subject: [PATCH 26/36] clean
---
R/boundsPostprob.R | 15 ++--
man/ocPredprob.Rd | 121 +++++++++++++++++++++++++++
tests/testthat/test-boundsPostProb.R | 36 ++++----
3 files changed, 143 insertions(+), 29 deletions(-)
diff --git a/R/boundsPostprob.R b/R/boundsPostprob.R
index b50fee96..e39be43f 100644
--- a/R/boundsPostprob.R
+++ b/R/boundsPostprob.R
@@ -25,16 +25,11 @@
#' @example examples/boundsPostprob.R
#' @export
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"
- ))
+ z <- matrix(NA, nrow = length(nvec), ncol = 8)
znames <- c(
"xL", "pL", "postL", "pL_upper_ci",
"xU", "pU", "postU", "pU_lower_ci"
)
- z <- matrix(NA, length(nvec), length(znames))
dimnames(z) <- list(nvec, znames)
k <- 0
for (n in nvec) {
@@ -44,12 +39,12 @@ boundsPostprob <- function(nvec, p0, p1 = p0, tL, tU, a, b) {
xU <- NA
for (x in 0:n) {
postp <- 1 - postprob(x, n, p0, parE = c(a, b)) # futility look
- if (postp >= tL) {
+ if (postp >= tL) { # Rule is P(RR < p0) > tL
postL <- postp
xL <- x
}
- postp <- postprob(x, n, p0, parE = c(a, b)) # efficacy look
- if (postp >= tU) {
+ postp <- postprob(x, n, p1, parE = c(a, b)) # efficacy look
+ if (postp >= tU) { # Rule is P(RR > p1) > tU
postU <- postp
xU <- x
break
@@ -69,5 +64,5 @@ boundsPostprob <- function(nvec, p0, p1 = p0, tL, tU, a, b) {
pU_lower_ci
)
}
- return(round(data.frame(nvec, z), 4))
+ round(data.frame(nvec, z), 4)
}
diff --git a/man/ocPredprob.Rd b/man/ocPredprob.Rd
index 25c8d9e2..63e17a09 100644
--- a/man/ocPredprob.Rd
+++ b/man/ocPredprob.Rd
@@ -117,3 +117,124 @@ The criteria for Decision 2 for Futility looks are :
}
}
}
+\examples{
+# 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
+# - 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.
+
+# Decision 1 with no wiggle.
+set.seed(20)
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.4,
+ p0 = 0.25,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = FALSE,
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with wiggle.
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.4,
+ p0 = 0.25,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = c(10, 20),
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with separate Futility and Efficacy looks at interim and final without wiggle.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.4,
+ p0 = 0.25,
+ p1 = 0.2,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = FALSE,
+ nnF = c(10, 15, 20),
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with separate Futility and Efficacy looks at interim and final with wiggle.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.4,
+ p0 = 0.25,
+ p1 = 0.2,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = c(10, 15, 20),
+ decision1 = TRUE
+)
+result$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
+# - 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.
+
+# Decision 2 without wiggle.
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.6,
+ p0 = 0.25,
+ p1 = 0.25,
+ tT = 0.6,
+ tF = 0.6,
+ phiU = 0.8,
+ phiFu = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = FALSE,
+ nnF = c(10, 20),
+ decision1 = FALSE
+)
+result$oc
+
+# Decision 2 with wiggle and with Futility only at final with non-uniform beta prior parE.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.6,
+ p0 = 0.25,
+ p1 = 0.25,
+ tT = 0.6,
+ tF = 0.6,
+ phiL = 0.8,
+ phiU = 0.8,
+ parE = c(11, 19),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = 30,
+ decision1 = FALSE
+)
+result$oc
+}
diff --git a/tests/testthat/test-boundsPostProb.R b/tests/testthat/test-boundsPostProb.R
index 3d74d3bc..e31397a8 100644
--- a/tests/testthat/test-boundsPostProb.R
+++ b/tests/testthat/test-boundsPostProb.R
@@ -10,24 +10,22 @@ test_that("boundsPostProb gives correct result and list", {
b = 1
)
expected <- data.frame(
- list(
- nvec = c(10, 20, 30, 40),
- xL = c(1, 3, 5, 6),
- pL = c(0.1, 0.15, 0.1667, 0.15),
- postL = c(0.6779, 0.6296, 0.6069, 0.739),
- pL_upper_ci = c(0.3942, 0.3437, 0.319, 0.2747),
- xU = c(2, 5, 7, 9),
- pU = c(0.2, 0.25, 0.2333, 0.225),
- postU = c(0.6174, 0.7693, 0.73, 0.704),
- pU_lower_ci = c(0.0368, 0.1041, 0.115, 0.1227)
- )
+ nvec = c(10, 20, 30, 40),
+ xL = c(1, 3, 5, 6),
+ pL = c(0.1, 0.15, 0.1667, 0.15),
+ postL = c(0.6779, 0.6296, 0.6069, 0.739),
+ pL_upper_ci = c(0.3942, 0.3437, 0.319, 0.2747),
+ xU = c(2, 5, 7, 9),
+ pU = c(0.2, 0.25, 0.2333, 0.225),
+ postU = c(0.6174, 0.7693, 0.73, 0.704),
+ pU_lower_ci = c(0.0368, 0.1041, 0.115, 0.1227)
)
- expect_equal(result$xL, c(1, 3, 5, 6))
- expect_equal(result$pL, c(0.1, 0.15, 0.1667, 0.15))
- expect_equal(result$postL, c(0.6779, 0.6296, 0.6069, 0.739))
- expect_equal(result$pL_upper_ci, c(0.3942, 0.3437, 0.319, 0.2747))
- expect_equal(result$xU, c(2, 5, 7, 9))
- expect_equal(result$pU, c(0.2, 0.25, 0.2333, 0.225))
- expect_equal(result$postU, c(0.6174, 0.7693, 0.73, 0.704))
- expect_equal(result$pU_lower_ci, c(0.0368, 0.1041, 0.115, 0.1227))
+ expect_equal(result$xL, expected$xL)
+ expect_equal(result$pL, expected$pL)
+ expect_equal(result$postL, expected$postL)
+ expect_equal(result$pL_upper_ci, expected$pL_upper_ci)
+ expect_equal(result$xU, expected$xU)
+ expect_equal(result$pU, expected$pU)
+ expect_equal(result$postU, expected$postU)
+ expect_equal(result$pU_lower_ci, expected$pU_lower_ci)
})
From 27cfdc9c1645a83b71f755cb9d39389592ed0c64 Mon Sep 17 00:00:00 2001
From: "27856297+dependabot-preview[bot]@users.noreply.github.com"
<27856297+dependabot-preview[bot]@users.noreply.github.com>
Date: Sun, 22 Sep 2024 18:56:58 +0000
Subject: [PATCH 27/36] [skip roxygen] [skip vbump] Roxygen Man Pages Auto
Update
---
man/ocPredprob.Rd | 121 ----------------------------------------------
1 file changed, 121 deletions(-)
diff --git a/man/ocPredprob.Rd b/man/ocPredprob.Rd
index 63e17a09..25c8d9e2 100644
--- a/man/ocPredprob.Rd
+++ b/man/ocPredprob.Rd
@@ -117,124 +117,3 @@ The criteria for Decision 2 for Futility looks are :
}
}
}
-\examples{
-# 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
-# - 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.
-
-# Decision 1 with no wiggle.
-set.seed(20)
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.4,
- p0 = 0.25,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = FALSE,
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with wiggle.
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.4,
- p0 = 0.25,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = TRUE,
- nnF = c(10, 20),
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with separate Futility and Efficacy looks at interim and final without wiggle.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.4,
- p0 = 0.25,
- p1 = 0.2,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = FALSE,
- nnF = c(10, 15, 20),
- decision1 = TRUE
-)
-result$oc
-
-# Decision 1 with separate Futility and Efficacy looks at interim and final with wiggle.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.4,
- p0 = 0.25,
- p1 = 0.2,
- tT = 0.6,
- phiL = 0.2,
- phiU = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = TRUE,
- nnF = c(10, 15, 20),
- decision1 = TRUE
-)
-result$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
-# - 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.
-
-# Decision 2 without wiggle.
-result <- ocPredprob(
- nnE = c(10, 20),
- truep = 0.6,
- p0 = 0.25,
- p1 = 0.25,
- tT = 0.6,
- tF = 0.6,
- phiU = 0.8,
- phiFu = 0.8,
- parE = c(1, 1),
- sim = 50,
- wiggle = FALSE,
- nnF = c(10, 20),
- decision1 = FALSE
-)
-result$oc
-
-# Decision 2 with wiggle and with Futility only at final with non-uniform beta prior parE.
-result <- ocPredprob(
- nnE = c(10, 25, 30),
- truep = 0.6,
- p0 = 0.25,
- p1 = 0.25,
- tT = 0.6,
- tF = 0.6,
- phiL = 0.8,
- phiU = 0.8,
- parE = c(11, 19),
- sim = 50,
- wiggle = TRUE,
- nnF = 30,
- decision1 = FALSE
-)
-result$oc
-}
From b569a7a316a4ea7c51df1447aed87a5fc96d5a62 Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Mon, 23 Sep 2024 15:02:25 +0200
Subject: [PATCH 28/36] clean
---
R/plotBeta.R | 10 ++--
man/ocPredprob.Rd | 121 ++++++++++++++++++++++++++++++++++++++++++++++
2 files changed, 126 insertions(+), 5 deletions(-)
diff --git a/R/plotBeta.R b/R/plotBeta.R
index 88e2a5ea..8dfcf795 100644
--- a/R/plotBeta.R
+++ b/R/plotBeta.R
@@ -66,7 +66,6 @@ plotBetaDiff <- function(parY, # parameters of experimental arm
diff <- seq(from = -1, to = 1, length = 1000)
data <- data.frame(
grid = diff,
- xticks = seq(from = 0, to = 1, by = 0.25),
density = dbetadiff(z = diff, parY = parY, parX = parX)
)
data$Stop <- ifelse(diff > -1 & diff < Stop_cut, TRUE, FALSE)
@@ -98,14 +97,15 @@ plotBetaDiff <- function(parY, # parameters of experimental arm
ggplot2::ggtitle(plot_title)
if (shade == TRUE) {
- pbetadiff_plot +
- ggplot2::geom_area(data = filter(data, Go == TRUE), fill = "#009E73") +
- ggplot2::geom_area(data = filter(data, Stop == TRUE), fill = "#D55E00")
+ pbetadiff_plot <- pbetadiff_plot +
+ ggplot2::geom_area(data = data[data$Go == TRUE, ], fill = "#009E73") +
+ ggplot2::geom_area(data = data[data$Stop == TRUE, ], fill = "#D55E00")
}
if (note == TRUE) {
- pbetadiff_plot +
+ pbetadiff_plot <- pbetadiff_plot +
ggplot2::annotate("text", x = -0.5, y = 4.25, label = Go_label, colour = "#009E73") +
ggplot2::annotate("text", x = -0.5, y = 4.75, label = Stop_label, colour = "#D55E00")
}
+ pbetadiff_plot
}
diff --git a/man/ocPredprob.Rd b/man/ocPredprob.Rd
index 25c8d9e2..63e17a09 100644
--- a/man/ocPredprob.Rd
+++ b/man/ocPredprob.Rd
@@ -117,3 +117,124 @@ The criteria for Decision 2 for Futility looks are :
}
}
}
+\examples{
+# Here we illustrate an example for Decision 1 with the following assumptions :
+# True response rate or truep of the treatment group = 40\%
+# The following are the Final Stop rules respectively :
+# - Final look for Efficacy: Pr( response rate > 25\% ) > 60\% or P(response rate > p0) > tT
+# - Final look for Futility: Pr( response rate < 25\% ) < 60\% or P(response rate > p0) < tT
+# - Interim look for Efficacy: Pr( success at final ) > 80\% or P(success at final) > phiU
+# - Interim look for Futility: Pr( failure at final ) < 20\% or P(success at final) < phiL
+# We assume a prior of treatment arm parE = Beta(1,1), unless otherwise indicated.
+
+# Decision 1 with no wiggle.
+set.seed(20)
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.4,
+ p0 = 0.25,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = FALSE,
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with wiggle.
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.4,
+ p0 = 0.25,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = c(10, 20),
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with separate Futility and Efficacy looks at interim and final without wiggle.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.4,
+ p0 = 0.25,
+ p1 = 0.2,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = FALSE,
+ nnF = c(10, 15, 20),
+ decision1 = TRUE
+)
+result$oc
+
+# Decision 1 with separate Futility and Efficacy looks at interim and final with wiggle.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.4,
+ p0 = 0.25,
+ p1 = 0.2,
+ tT = 0.6,
+ phiL = 0.2,
+ phiU = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = c(10, 15, 20),
+ decision1 = TRUE
+)
+result$oc
+
+# Here we illustrate an example for Decision 2 with the following assumptions :
+# True response rate or truep of the treatment group = 60\%
+# The following are the Final Stop rules respectively :
+# - Final look for Efficacy: Pr( response rate > 25\% ) > 60\% or P(response rate > p0) > tT
+# - Final look for Futility: Pr( response rate < 25\% ) < 60\% or P(response rate < p1) > tF
+# - Interim look for Efficacy: Pr( success at final ) > 80\% or P(success at final) > phiU
+# - Interim look for Futility: Pr( failure at final ) > 80\% or P(failure at final) > phiFu
+# We assume a prior of treatment arm parE = Beta(1,1), unless otherwise indicated.
+
+# Decision 2 without wiggle.
+result <- ocPredprob(
+ nnE = c(10, 20),
+ truep = 0.6,
+ p0 = 0.25,
+ p1 = 0.25,
+ tT = 0.6,
+ tF = 0.6,
+ phiU = 0.8,
+ phiFu = 0.8,
+ parE = c(1, 1),
+ sim = 50,
+ wiggle = FALSE,
+ nnF = c(10, 20),
+ decision1 = FALSE
+)
+result$oc
+
+# Decision 2 with wiggle and with Futility only at final with non-uniform beta prior parE.
+result <- ocPredprob(
+ nnE = c(10, 25, 30),
+ truep = 0.6,
+ p0 = 0.25,
+ p1 = 0.25,
+ tT = 0.6,
+ tF = 0.6,
+ phiL = 0.8,
+ phiU = 0.8,
+ parE = c(11, 19),
+ sim = 50,
+ wiggle = TRUE,
+ nnF = 30,
+ decision1 = FALSE
+)
+result$oc
+}
From db9412de0505bb5006da18017f974b7729b0b985 Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Fri, 24 May 2024 14:00:43 +0200
Subject: [PATCH 29/36] clean
---
R/boundsPostprob.R | 5 +++++
man/boundsPostprob.Rd | 12 ++++++++++++
2 files changed, 17 insertions(+)
diff --git a/R/boundsPostprob.R b/R/boundsPostprob.R
index e39be43f..eb490e67 100644
--- a/R/boundsPostprob.R
+++ b/R/boundsPostprob.R
@@ -5,6 +5,9 @@
#' 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
#'
+#' 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
@@ -15,11 +18,13 @@
#' - `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
+#' - `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`.
#' - `postU` : posterior probability corresponding to `xU`.
#' - `pU_lower_ci` : lower bound of one sided 95% CI for the response rate `pU` based on exact
+#' - `LciU` : lower bound of one sided 95% CI for the response rate based on exact
#' binomial test.
#'
#' @example examples/boundsPostprob.R
diff --git a/man/boundsPostprob.Rd b/man/boundsPostprob.Rd
index 4394ef94..9a664fb9 100644
--- a/man/boundsPostprob.Rd
+++ b/man/boundsPostprob.Rd
@@ -24,12 +24,20 @@ A matrix for each same size in \code{nvec}. For each sample size, the following
threshold
\item \code{pL} : response rate corresponding to \code{xL}.
\item \code{postL}: posterior probability corresponding to \code{xL}.
+<<<<<<< HEAD
\item \code{pL_upper_ci} : upper bound of one sided 95\% CI for the response rate \code{pL} based on an
+=======
+\item \code{Ucil} : upper bound of one sided 95\% CI for the response rate based on an
+>>>>>>> 008502d (clean)
exact binomial test.
\item \code{xU} : the minimal number of responses that meet the efficacy threshold.
\item \code{pU} : response rate corresponding to \code{xU}.
\item \code{postU} : posterior probability corresponding to \code{xU}.
+<<<<<<< HEAD
\item \code{pU_lower_ci} : lower bound of one sided 95\% CI for the response rate \code{pU} based on exact
+=======
+\item \code{LciU} : lower bound of one sided 95\% CI for the response rate based on exact
+>>>>>>> 008502d (clean)
binomial test.
}
}
@@ -39,6 +47,10 @@ 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
}
+\details{
+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
+}
\examples{
# 40 pts trial with interim looks after each 10 pts.,
# Efficacy decision if more than 60\% probability to be above 20\% ORR,
From 6484d12727e09e28d7b7cd2009b3fb2e70c76265 Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Mon, 23 Sep 2024 15:18:16 +0200
Subject: [PATCH 30/36] clean
---
inst/WORDLIST | 3 +++
man/ocPredprob.Rd | 3 +++
2 files changed, 6 insertions(+)
diff --git a/inst/WORDLIST b/inst/WORDLIST
index 86e46e6c..be3cf716 100644
--- a/inst/WORDLIST
+++ b/inst/WORDLIST
@@ -202,6 +202,8 @@ renewcommand
reproducibility
responder
responders
+roxygen
+Roxygen
Sabanes
sabanes
Sabanés
@@ -244,6 +246,7 @@ USUBJID
VAD
vanillaBayes
vanillaPP
+vbump
Vehtari
WeightedBayes
weightedBetaPrior
diff --git a/man/ocPredprob.Rd b/man/ocPredprob.Rd
index 63e17a09..d6480b8d 100644
--- a/man/ocPredprob.Rd
+++ b/man/ocPredprob.Rd
@@ -117,6 +117,7 @@ The criteria for Decision 2 for Futility looks are :
}
}
}
+<<<<<<< HEAD
\examples{
# Here we illustrate an example for Decision 1 with the following assumptions :
# True response rate or truep of the treatment group = 40\%
@@ -238,3 +239,5 @@ result <- ocPredprob(
)
result$oc
}
+=======
+>>>>>>> ab51030 ([skip roxygen] [skip vbump] Roxygen Man Pages Auto Update)
From 21f0ee4fd91a8766fc769e760057098721052ddf Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Thu, 2 Jan 2025 16:43:52 +0100
Subject: [PATCH 31/36] clean
---
R/plotBeta.R | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/R/plotBeta.R b/R/plotBeta.R
index 8dfcf795..792fc0c0 100644
--- a/R/plotBeta.R
+++ b/R/plotBeta.R
@@ -30,7 +30,7 @@ plotBeta <- function(alpha, beta, ...) {
ggplot2::scale_x_continuous(labels = scales::percent_format())
}
-#' Plot Diff Between two Beta distributions
+#' Plot difference Between two Beta distributions
#'
#' This function will plot the PDF of a difference between two Beta distributions
#'
@@ -50,7 +50,7 @@ plotBeta <- function(alpha, beta, ...) {
#' additional arguments to `ggplot()`
#' @return a ggplot object
#'
-#' @example examples/myPlotDiff.R
+#' @example examples/plotBetaDiff.R
#'
#' @importFrom graphics par axis polygon mtext
#' @importFrom stats integrate
From f922d4669d1bf4fde0f4e3af397967ff0010ebd8 Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Thu, 2 Jan 2025 22:23:26 +0100
Subject: [PATCH 32/36] clean
---
R/plotBeta.R | 31 ++++++++++++++++++-------------
examples/myPlotDiff.R | 8 --------
examples/plotBetaDiff.R | 3 ++-
man/ocPredprob.Rd | 16 ++++++++--------
man/plotBetaDiff.Rd | 22 +++++++++++++---------
5 files changed, 41 insertions(+), 39 deletions(-)
delete mode 100644 examples/myPlotDiff.R
diff --git a/R/plotBeta.R b/R/plotBeta.R
index 792fc0c0..da475c60 100644
--- a/R/plotBeta.R
+++ b/R/plotBeta.R
@@ -86,22 +86,27 @@ plotBetaDiff <- function(parY, # parameters of experimental arm
upper = Stop_cut # Calculate probability of Stop, if difference was at most `Stop_cut`.
)
- Go_label <- paste("Probability of Go is", round(Go_auc$value * 100, digits = 2), "%")
- Stop_label <- paste("Probability of Stop is", round(Stop_auc$value * 100, digits = 2), "%")
- plot_title <- paste("According to Beta difference density", Go_label, "and", Stop_label)
-
- pbetadiff_plot <- ggplot2::ggplot(data = data, mapping = aes(x = grid, y = density)) +
- ggplot2::geom_line(colour = "#888888") +
- xlab("Difference between treatment") +
- ggplot2::ylab(quote(f(x))) +
- ggplot2::ggtitle(plot_title)
+ Go_label <- paste("probability of Go is", round(Go_auc$value * 100, digits = 2), "%")
+ Stop_label <- paste("probability of Stop is", round(Stop_auc$value * 100, digits = 2), "%")
+ plot_title <- paste("According to Beta difference density", Go_label, "and\n", Stop_label)
if (shade == TRUE) {
- pbetadiff_plot <- pbetadiff_plot +
- ggplot2::geom_area(data = data[data$Go == TRUE, ], fill = "#009E73") +
- ggplot2::geom_area(data = data[data$Stop == TRUE, ], fill = "#D55E00")
+ pbetadiff_plot <- ggplot2::ggplot(data = data, aes(x = grid, y = density)) +
+ ggplot2::geom_line(colour = "#888888") +
+ geom_area(data = data[data$grid < Stop_cut,], fill = "#D55E00",
+ mapping = aes(x = ifelse(grid < 0.2 & grid < 0.5, grid, 0))) +
+ geom_area(data = data[data$grid > Go_cut,], fill = "#009E73",
+ mapping = aes(x = ifelse(grid > 0.3, grid, 0))) +
+ xlab("Difference between treatment") +
+ ggplot2::ylab(quote(f(x))) +
+ ggplot2::ggtitle(plot_title)
+ } else {
+ pbetadiff_plot <- ggplot2::ggplot(data = data, aes(x = grid, y = density)) +
+ ggplot2::geom_line(colour = "#888888") +
+ xlab("Difference between treatment") +
+ ggplot2::ylab(quote(f(x))) +
+ ggplot2::ggtitle(plot_title)
}
-
if (note == TRUE) {
pbetadiff_plot <- pbetadiff_plot +
ggplot2::annotate("text", x = -0.5, y = 4.25, label = Go_label, colour = "#009E73") +
diff --git a/examples/myPlotDiff.R b/examples/myPlotDiff.R
deleted file mode 100644
index 991f9fc2..00000000
--- a/examples/myPlotDiff.R
+++ /dev/null
@@ -1,8 +0,0 @@
-myPlotDiff(
- parY = c(5, 10),
- parX = c(2, 5),
- cut_B = 0.2, # a meaningful improvement threshold
- cut_W = 0.05, # a poor improvement threshold
- shade = 1, # paint the two areas under the curve, default: yes. other numbers stands for "no";
- note = 1
-) # show values of the colored area, default: yes. other numbers stands for "no";
diff --git a/examples/plotBetaDiff.R b/examples/plotBetaDiff.R
index 0a22a11c..fd8726ec 100644
--- a/examples/plotBetaDiff.R
+++ b/examples/plotBetaDiff.R
@@ -1,4 +1,5 @@
-# plotBetDiff
+# The beta distribution and acceptable bounds for
+# a meaningful improvement of 0.20 and worsening of 0.05
parX <- c(1, 52) # parameters of experimental arm
parY <- c(5.5, 20.5) # parameters of control or SOC
plotBetaDiff(
diff --git a/man/ocPredprob.Rd b/man/ocPredprob.Rd
index 05595ace..63e17a09 100644
--- a/man/ocPredprob.Rd
+++ b/man/ocPredprob.Rd
@@ -97,8 +97,8 @@ The criteria for Decision 1 for Interim looks are :
The criteria for Decision 1 for Final looks are:
\itemize{
-\item Final GO = P( RR > p0 | data) > tT
-\item Final STOP = P( RR > p0 | data ) < tT
+\item Final GO = P( response rate > p0 | data) > tT
+\item Final STOP = P( response rate > p0 | data ) < tT
}
}
@@ -112,8 +112,8 @@ The criteria for Decision 2 for Interim looks are :
The criteria for Decision 2 for Futility looks are :
\itemize{
-\item Final GO = P( RR > p0) > tT
-\item Final STOP = P( RR < p1) > tF
+\item Final GO = P( response rate > p0) > tT
+\item Final STOP = P( response rate < p1) > tF
}
}
}
@@ -121,8 +121,8 @@ The criteria for Decision 2 for Futility looks are :
# 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( RR > 25\% ) > 60\% or P( RR > p0) > tT
-# - Final look for Futility: Pr( RR < 25\% ) < 60\% or P(RR > p0) < tT
+# - 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
# - 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.
@@ -196,8 +196,8 @@ 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( RR > 25\% ) > 60\% or P(RR > p0) > tT
-# - Final look for Futility: Pr( RR < 25\% ) < 60\% or P(RR < p1) > tF
+# - 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
# - 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.
diff --git a/man/plotBetaDiff.Rd b/man/plotBetaDiff.Rd
index 6c7b9c4d..32c0cb7f 100644
--- a/man/plotBetaDiff.Rd
+++ b/man/plotBetaDiff.Rd
@@ -2,7 +2,7 @@
% Please edit documentation in R/plotBeta.R
\name{plotBetaDiff}
\alias{plotBetaDiff}
-\title{Plot Diff Between two Beta distributions}
+\title{Plot difference Between two Beta distributions}
\usage{
plotBetaDiff(
parY,
@@ -35,13 +35,17 @@ a ggplot object
This function will plot the PDF of a difference between two Beta distributions
}
\examples{
-myPlotDiff(
- parY = c(5, 10),
- parX = c(2, 5),
- cut_B = 0.2, # a meaningful improvement threshold
- cut_W = 0.05, # a poor improvement threshold
- shade = 1, # paint the two areas under the curve, default: yes. other numbers stands for "no";
- note = 1
-) # show values of the colored area, default: yes. other numbers stands for "no";
+# The beta distribution and acceptable bounds for
+# a meaningful improvement of 0.20 and worsening of 0.05
+parX <- c(1, 52) # parameters of experimental arm
+parY <- c(5.5, 20.5) # parameters of control or SOC
+plotBetaDiff(
+ parY = parY,
+ parX = parX,
+ Go_cut = 0.3,
+ Stop_cut = 0.1,
+ shade = TRUE,
+ note = TRUE
+)
}
\keyword{graphics}
From 01b8185ab74e712fb92ab3835d241054a70124d1 Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Fri, 3 Jan 2025 14:39:38 +0100
Subject: [PATCH 33/36] plot and examples added
---
R/plotBeta.R | 12 ++++++------
examples/plotBetaDiff.R | 19 +++++++++++++++----
man/plotBetaDiff.Rd | 19 +++++++++++++++----
3 files changed, 36 insertions(+), 14 deletions(-)
diff --git a/R/plotBeta.R b/R/plotBeta.R
index da475c60..d4d71a77 100644
--- a/R/plotBeta.R
+++ b/R/plotBeta.R
@@ -86,14 +86,14 @@ plotBetaDiff <- function(parY, # parameters of experimental arm
upper = Stop_cut # Calculate probability of Stop, if difference was at most `Stop_cut`.
)
- Go_label <- paste("probability of Go is", round(Go_auc$value * 100, digits = 2), "%")
- Stop_label <- paste("probability of Stop is", round(Stop_auc$value * 100, digits = 2), "%")
- plot_title <- paste("According to Beta difference density", Go_label, "and\n", Stop_label)
+ Go_label <- paste("P(Go) is", round(Go_auc$value * 100, digits = 2), "%")
+ Stop_label <- paste("P(S) is", round(Stop_auc$value * 100, digits = 2), "%")
+ plot_title <- paste("According to Beta difference density", Go_label, "and", Stop_label)
if (shade == TRUE) {
pbetadiff_plot <- ggplot2::ggplot(data = data, aes(x = grid, y = density)) +
ggplot2::geom_line(colour = "#888888") +
- geom_area(data = data[data$grid < Stop_cut,], fill = "#D55E00",
+ geom_area(data = data[data$grid < Stop_cut,], fill = "#FF0046",
mapping = aes(x = ifelse(grid < 0.2 & grid < 0.5, grid, 0))) +
geom_area(data = data[data$grid > Go_cut,], fill = "#009E73",
mapping = aes(x = ifelse(grid > 0.3, grid, 0))) +
@@ -109,8 +109,8 @@ plotBetaDiff <- function(parY, # parameters of experimental arm
}
if (note == TRUE) {
pbetadiff_plot <- pbetadiff_plot +
- ggplot2::annotate("text", x = -0.5, y = 4.25, label = Go_label, colour = "#009E73") +
- ggplot2::annotate("text", x = -0.5, y = 4.75, label = Stop_label, colour = "#D55E00")
+ ggplot2::annotate("text", x = -0.5, y = 3.75, size = 5, label = Stop_label, colour = "#FF0046") +
+ ggplot2::annotate("text", x = -0.5, y = 3.25, size = 5, label = Go_label, colour = "#009E73")
}
pbetadiff_plot
}
diff --git a/examples/plotBetaDiff.R b/examples/plotBetaDiff.R
index fd8726ec..277e175e 100644
--- a/examples/plotBetaDiff.R
+++ b/examples/plotBetaDiff.R
@@ -1,12 +1,23 @@
# The beta distribution and acceptable bounds for
-# a meaningful improvement of 0.20 and worsening of 0.05
-parX <- c(1, 52) # parameters of experimental arm
-parY <- c(5.5, 20.5) # parameters of control or SOC
+# a meaningful improvement of 0.20 and worsening of 0.1
+parX <- c(1, 52) # prior parameters of experimental arm
+parY <- c(5.5, 20.5) # prior parameters of control or SOC
plotBetaDiff(
parY = parY,
parX = parX,
Go_cut = 0.3,
- Stop_cut = 0.1,
+ Stop_cut = 0.1, # below a difference of 10%, is an unsuccesful trial
+ shade = TRUE,
+ note = TRUE
+)
+
+
+# a larger Go_cut with uniform prior
+plotBetaDiff(
+ parY = c(1, 1), # prior parameters for experimental arm
+ parX = c(1, 1), # prior parameters for control or SOC arm
+ Go_cut = 0.3,
+ Stop_cut = 0.1, # below a difference of 10%, is an unsuccesful trial
shade = TRUE,
note = TRUE
)
diff --git a/man/plotBetaDiff.Rd b/man/plotBetaDiff.Rd
index 32c0cb7f..ac715de5 100644
--- a/man/plotBetaDiff.Rd
+++ b/man/plotBetaDiff.Rd
@@ -36,14 +36,25 @@ This function will plot the PDF of a difference between two Beta distributions
}
\examples{
# The beta distribution and acceptable bounds for
-# a meaningful improvement of 0.20 and worsening of 0.05
-parX <- c(1, 52) # parameters of experimental arm
-parY <- c(5.5, 20.5) # parameters of control or SOC
+# a meaningful improvement of 0.20 and worsening of 0.1
+parX <- c(1, 52) # prior parameters of experimental arm
+parY <- c(5.5, 20.5) # prior parameters of control or SOC
plotBetaDiff(
parY = parY,
parX = parX,
Go_cut = 0.3,
- Stop_cut = 0.1,
+ Stop_cut = 0.1, # below a difference of 10\%, is an unsuccesful trial
+ shade = TRUE,
+ note = TRUE
+)
+
+
+# a larger Go_cut with uniform prior
+plotBetaDiff(
+ parY = c(1, 1), # prior parameters for experimental arm
+ parX = c(1, 1), # prior parameters for control or SOC arm
+ Go_cut = 0.3,
+ Stop_cut = 0.1, # below a difference of 10\%, is an unsuccesful trial
shade = TRUE,
note = TRUE
)
From c475a98d816fa45dda6bddad34a8ab401625671d Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Fri, 3 Jan 2025 14:59:31 +0100
Subject: [PATCH 34/36] for PR review before .svg files for test
---
tests/testthat/test-plotBetaDiff.R | 22 ++++++++++++++++++++++
1 file changed, 22 insertions(+)
create mode 100644 tests/testthat/test-plotBetaDiff.R
diff --git a/tests/testthat/test-plotBetaDiff.R b/tests/testthat/test-plotBetaDiff.R
new file mode 100644
index 00000000..e7c2a091
--- /dev/null
+++ b/tests/testthat/test-plotBetaDiff.R
@@ -0,0 +1,22 @@
+# plotBetaDiff
+test_that("plotBetaDiff works as expected", {
+ result <- plotBetaDiff(
+ parY = c(1, 1),
+ parX = c(6, 10),
+ Go_cut = 0.3,
+ Stop_cut = 0.1, # below a difference of 10%, is an unsuccesful trial
+ shade = TRUE,
+ note = TRUE
+ )
+ result <- plotBetaDiff(
+ parY = c(2, 3),
+ parX = c(4, 10),
+ Go_cut = 0.3,
+ Stop_cut = 0.1, # below a difference of 10%, is an unsuccesful trial
+ shade = TRUE,
+ note = TRUE
+ )
+ vdiffr::expect_doppelganger("", result)
+ vdiffr::expect_doppelganger("", result)
+
+})
From e500868b3d2d5ef5596175d338b493c1d6a867dc Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Tue, 7 Jan 2025 10:50:31 +0100
Subject: [PATCH 35/36] some first workings
---
R/plotOc.R | 25 +++++++++++++++++++----
examples/plotOc.R | 45 +++++++++++++++++++++++++++++++++++++----
man/plotOc.Rd | 51 ++++++++++++++++++++++++++++++++++++++++-------
3 files changed, 106 insertions(+), 15 deletions(-)
diff --git a/R/plotOc.R b/R/plotOc.R
index c4765cdd..9806db9a 100644
--- a/R/plotOc.R
+++ b/R/plotOc.R
@@ -1,9 +1,10 @@
#' Display the operating characteristics using an oc object
#'
-#' Reads results from \code{\link{ocPredprob}}, \code{\link{ocPostprob}}
+#' Reads results from [ocPredprob()]
#' etc. and displays a bar plot of the operating characteristics
#'
-#' @param z returned oc value
+#' @typed oc : list
+#' returned oc parameters
#' @return nothing, only plots as side effect
#'
#' @importFrom graphics barplot title
@@ -11,9 +12,25 @@
#' @example examples/plotOc.R
#' @export
#' @keywords graphics
-plotOc <- function(z) {
+plotOc <- function(oc) {
+
+ if (wiggle == FALSE) {
+ data <- table(oc$Decision, oc$SampleSize) / oc$params$sim
+ } else {
+
+ }
+
+ggplot(oc, aes(x = name, y = value)) +
+ geom_bar(stat = "identity") +
+ ggtitle("Percentage of trials that Go and Stop per look") +
+ ylabs("Percentage %") +
+ xlabs("Looks and sample size")
+
+
+
## plot function for oc.predprob or oc.postprob, or the dist versions of them
- graphics::barplot(table(z$Decision, z$SampleSize) / z$params$sim, beside = TRUE)
+ graphics::barplot(table(oc$Decision, oc$SampleSize) / oc$params$sim, beside = TRUE)
+
## get the parameter
parDat <- lapply(z$params, deparse)
diff --git a/examples/plotOc.R b/examples/plotOc.R
index 930ca464..647979d1 100644
--- a/examples/plotOc.R
+++ b/examples/plotOc.R
@@ -1,9 +1,46 @@
# get operating character result from oc.postprob
-res1 <- ocPostprob(
+oc <- ocPostprob(
nnE = c(10, 20, 30), truep = 0.4, p0 = 0.2,
- p1 = 0.3, tL = 0.6, tU = 0.8, parE = c(1, 1), sim = 50000
+ p1 = 0.3, tL = 0.6, tU = 0.8, parE = c(1, 1), sim = 100, wiggle = FALSE
)
-res1$oc
+oc$oc
+# plotOc(oc)
-plotOc(res1)
+
+oc <- ocPostprob(
+ nnE = c(10, 20, 30), truep = 0.4, p0 = 0.2,
+ p1 = 0.3, tL = 0.6, tU = 0.8, parE = c(1, 1), sim = 100, wiggle = TRUE
+)
+oc$oc
+
+data <- data.frame(Decision = as.factor(oc$Decision),
+ Looks = oc$SampleSize)
+
+table(data$Decision, data$Looks)
+
+data %>% group_by(Decision, Looks) %>% summarise(n = n())
+
+data %>% group_by(SampleSize) %>% summarise(n = n())
+
+ggplot(dat, aes(x = SampleSize, y = Decision)) +
+ geom_bar(stat = "identity")
+
+data %>% group_by(Looks, Decision) %>% summarise(tot = n())
+
+
+table(oc$Decision, oc$SampleSize) / oc$params$sim
+table(oc$Decision, oc$SampleSize)
+
+oc$Decision <- ifelse(oc$Decision == TRUE, "GO",
+ ifelse(oc$Decision == FALSE, "Stop", "Grey"))
+
+oc$Decision
+
+data.frame(looks = c(res1$wiggled_nnrE))
+
+table(oc$Decision, oc$SampleSize)
+
+tt = data.frame(Decision = res$Decision,
+ look = res$SampleSize)
+tt %>% group_by(look) %>% summarise(success = per())
diff --git a/man/plotOc.Rd b/man/plotOc.Rd
index 16389213..76965376 100644
--- a/man/plotOc.Rd
+++ b/man/plotOc.Rd
@@ -4,27 +4,64 @@
\alias{plotOc}
\title{Display the operating characteristics using an oc object}
\usage{
-plotOc(z)
+plotOc(oc)
}
\arguments{
-\item{z}{returned oc value}
+\item{oc}{(\code{list}):\cr returned oc parameters}
}
\value{
nothing, only plots as side effect
}
\description{
-Reads results from \code{\link{ocPredprob}}, \code{\link{ocPostprob}}
+Reads results from \code{\link[=ocPredprob]{ocPredprob()}}
etc. and displays a bar plot of the operating characteristics
}
\examples{
# get operating character result from oc.postprob
-res1 <- ocPostprob(
+oc <- ocPostprob(
nnE = c(10, 20, 30), truep = 0.4, p0 = 0.2,
- p1 = 0.3, tL = 0.6, tU = 0.8, parE = c(1, 1), sim = 50000
+ p1 = 0.3, tL = 0.6, tU = 0.8, parE = c(1, 1), sim = 100, wiggle = FALSE
)
-res1$oc
+oc$oc
+# plotOc(oc)
-plotOc(res1)
+
+oc <- ocPostprob(
+ nnE = c(10, 20, 30), truep = 0.4, p0 = 0.2,
+ p1 = 0.3, tL = 0.6, tU = 0.8, parE = c(1, 1), sim = 100, wiggle = TRUE
+)
+oc$oc
+
+data <- data.frame(Decision = as.factor(oc$Decision),
+ Looks = oc$SampleSize)
+
+table(data$Decision, data$Looks)
+
+data \%>\% group_by(Decision, Looks) \%>\% summarise(n = n())
+
+data \%>\% group_by(SampleSize) \%>\% summarise(n = n())
+
+ggplot(dat, aes(x = SampleSize, y = Decision)) +
+ geom_bar(stat = "identity")
+
+data \%>\% group_by(Looks, Decision) \%>\% summarise(tot = n())
+
+
+table(oc$Decision, oc$SampleSize) / oc$params$sim
+table(oc$Decision, oc$SampleSize)
+
+oc$Decision <- ifelse(oc$Decision == TRUE, "GO",
+ ifelse(oc$Decision == FALSE, "Stop", "Grey"))
+
+oc$Decision
+
+data.frame(looks = c(res1$wiggled_nnrE))
+
+table(oc$Decision, oc$SampleSize)
+
+tt = data.frame(Decision = res$Decision,
+ look = res$SampleSize)
+tt \%>\% group_by(look) \%>\% summarise(success = per())
}
\keyword{graphics}
From 69e8517955dcb840495edce7e911138f8270a6e1 Mon Sep 17 00:00:00 2001
From: Audrey Yeo <comptesaudrey@gmail.com>
Date: Tue, 7 Jan 2025 11:05:34 +0100
Subject: [PATCH 36/36] clean git commit -m clean
---
examples/plotOc.R | 30 ------------------------------
1 file changed, 30 deletions(-)
diff --git a/examples/plotOc.R b/examples/plotOc.R
index 647979d1..53216078 100644
--- a/examples/plotOc.R
+++ b/examples/plotOc.R
@@ -14,33 +14,3 @@ oc <- ocPostprob(
)
oc$oc
-data <- data.frame(Decision = as.factor(oc$Decision),
- Looks = oc$SampleSize)
-
-table(data$Decision, data$Looks)
-
-data %>% group_by(Decision, Looks) %>% summarise(n = n())
-
-data %>% group_by(SampleSize) %>% summarise(n = n())
-
-ggplot(dat, aes(x = SampleSize, y = Decision)) +
- geom_bar(stat = "identity")
-
-data %>% group_by(Looks, Decision) %>% summarise(tot = n())
-
-
-table(oc$Decision, oc$SampleSize) / oc$params$sim
-table(oc$Decision, oc$SampleSize)
-
-oc$Decision <- ifelse(oc$Decision == TRUE, "GO",
- ifelse(oc$Decision == FALSE, "Stop", "Grey"))
-
-oc$Decision
-
-data.frame(looks = c(res1$wiggled_nnrE))
-
-table(oc$Decision, oc$SampleSize)
-
-tt = data.frame(Decision = res$Decision,
- look = res$SampleSize)
-tt %>% group_by(look) %>% summarise(success = per())