Nothing
## |
## | *Simulation of binary data with group sequential and combination test*
## |
## | This file is part of the R package rpact:
## | Confirmatory Adaptive Clinical Trial Design and Analysis
## |
## | Author: Gernot Wassmer, PhD, and Friedrich Pahlke, PhD
## | Licensed under "GNU Lesser General Public License" version 3
## | License text can be found here: https://www.r-project.org/Licenses/LGPL-3
## |
## | RPACT company website: https://www.rpact.com
## | rpact package website: https://www.rpact.org
## |
## | Contact us for information about our services: info@rpact.com
## |
## | File version: $Revision: 7126 $
## | Last changed: $Date: 2023-06-23 14:26:39 +0200 (Fr, 23 Jun 2023) $
## | Last changed by: $Author: pahlke $
## |
.getSimulationRatesStageSubjects <- function(
...,
stage,
riskRatio,
thetaH0,
groups,
plannedSubjects,
directionUpper,
allocationRatioPlanned,
minNumberOfSubjectsPerStage,
maxNumberOfSubjectsPerStage,
sampleSizesPerStage,
conditionalPower,
conditionalCriticalValue,
overallRate,
farringtonManningValue1,
farringtonManningValue2) {
if (is.na(conditionalPower)) {
return(plannedSubjects[stage] - plannedSubjects[stage - 1])
}
if (groups == 1) {
stageSubjects <-
(max(0, conditionalCriticalValue * sqrt(thetaH0 * (1 - thetaH0)) +
.getQNorm(conditionalPower) * sqrt(overallRate[1] * (1 - overallRate[1]))))^2 /
(max(1e-12, (2 * directionUpper - 1) * (overallRate[1] - thetaH0)))^2
} else {
mult <- 1
corr <- thetaH0
if (riskRatio) {
mult <- thetaH0
corr <- 0
}
stageSubjects <- (1 + 1 / allocationRatioPlanned[stage]) * (max(0, conditionalCriticalValue *
sqrt(farringtonManningValue1 * (1 - farringtonManningValue1) +
farringtonManningValue2 * (1 - farringtonManningValue2) * allocationRatioPlanned[stage] * mult^2) +
.getQNorm(conditionalPower) * sqrt(overallRate[1] * (1 - overallRate[1]) + overallRate[2] *
(1 - overallRate[2]) * allocationRatioPlanned[stage] * mult^2)))^2 /
(max(1e-12, (2 * directionUpper - 1) * (overallRate[1] - mult * overallRate[2] - corr)))^2
}
stageSubjects <- ceiling(min(max(minNumberOfSubjectsPerStage[stage], stageSubjects), maxNumberOfSubjectsPerStage[stage]))
return(stageSubjects)
}
#' @title
#' Get Simulation Rates
#'
#' @description
#' Returns the simulated power, stopping probabilities, conditional power, and expected sample size for
#' testing rates in a one or two treatment groups testing situation.
#'
#' @inheritParams param_design_with_default
#' @inheritParams param_groups
#' @inheritParams param_normalApproximation
#' @param riskRatio If \code{TRUE}, the design characteristics for
#' one-sided testing of H0: \code{pi1 / pi2 = thetaH0} are simulated, default is \code{FALSE}.
#' @inheritParams param_thetaH0
#' @inheritParams param_pi1_rates
#' @inheritParams param_pi2_rates
#' @inheritParams param_directionUpper
#' @inheritParams param_allocationRatioPlanned
#' @inheritParams param_plannedSubjects
#' @inheritParams param_minNumberOfSubjectsPerStage
#' @inheritParams param_maxNumberOfSubjectsPerStage
#' @inheritParams param_conditionalPowerSimulation
#' @param pi1H1 If specified, the assumed probability in the active treatment group if two treatment groups
#' are considered, or the assumed probability for a one treatment group design, for which the conditional
#' power was calculated.
#' @param pi2H1 If specified, the assumed probability in the reference group if two treatment groups
#' are considered, for which the conditional power was calculated.
#' @inheritParams param_maxNumberOfIterations
#' @inheritParams param_calcSubjectsFunction
#' @inheritParams param_seed
#' @inheritParams param_three_dots
#' @inheritParams param_showStatistics
#'
#' @details
#' At given design the function simulates the power, stopping probabilities, conditional power, and expected
#' sample size at given number of subjects and parameter configuration.
#' Additionally, an allocation ratio = n1/n2 can be specified where n1 and n2 are the number
#' of subjects in the two treatment groups.
#'
#' The definition of \code{pi1H1} and/or \code{pi2H1} makes only sense if \code{kMax} > 1
#' and if \code{conditionalPower}, \code{minNumberOfSubjectsPerStage}, and
#' \code{maxNumberOfSubjectsPerStage} (or \code{calcSubjectsFunction}) are defined.
#'
#' \code{calcSubjectsFunction}\cr
#' This function returns the number of subjects at given conditional power and conditional critical value for specified
#' testing situation. The function might depend on variables
#' \code{stage},
#' \code{riskRatio},
#' \code{thetaH0},
#' \code{groups},
#' \code{plannedSubjects},
#' \code{sampleSizesPerStage},
#' \code{directionUpper},
#' \code{allocationRatioPlanned},
#' \code{minNumberOfSubjectsPerStage},
#' \code{maxNumberOfSubjectsPerStage},
#' \code{conditionalPower},
#' \code{conditionalCriticalValue},
#' \code{overallRate},
#' \code{farringtonManningValue1}, and \code{farringtonManningValue2}.
#' The function has to contain the three-dots argument '...' (see examples).
#'
#' @section Simulation Data:
#' The summary statistics "Simulated data" contains the following parameters: median [range]; mean +/-sd\cr
#'
#' \code{$show(showStatistics = FALSE)} or \code{$setShowStatistics(FALSE)} can be used to disable
#' the output of the aggregated simulated data.\cr
#'
#' Example 1: \cr
#' \code{simulationResults <- getSimulationRates(plannedSubjects = 40)} \cr
#' \code{simulationResults$show(showStatistics = FALSE)}\cr
#'
#' Example 2: \cr
#' \code{simulationResults <- getSimulationRates(plannedSubjects = 40)} \cr
#' \code{simulationResults$setShowStatistics(FALSE)}\cr
#' \code{simulationResults}\cr
#'
#' \code{\link[=getData]{getData()}} can be used to get the aggregated simulated data from the
#' object as \code{\link[base]{data.frame}}. The data frame contains the following columns:
#' \enumerate{
#' \item \code{iterationNumber}: The number of the simulation iteration.
#' \item \code{stageNumber}: The stage.
#' \item \code{pi1}: The assumed or derived event rate in the treatment group (if available).
#' \item \code{pi2}: The assumed or derived event rate in the control group (if available).
#' \item \code{numberOfSubjects}: The number of subjects under consideration when the
#' (interim) analysis takes place.
#' \item \code{rejectPerStage}: 1 if null hypothesis can be rejected, 0 otherwise.
#' \item \code{futilityPerStage}: 1 if study should be stopped for futility, 0 otherwise.
#' \item \code{testStatistic}: The test statistic that is used for the test decision,
#' depends on which design was chosen (group sequential, inverse normal,
#' or Fisher combination test)'
#' \item \code{testStatisticsPerStage}: The test statistic for each stage if only data from
#' the considered stage is taken into account.
#' \item \code{overallRate1}: The cumulative rate in treatment group 1.
#' \item \code{overallRate2}: The cumulative rate in treatment group 2.
#' \item \code{stagewiseRates1}: The stage-wise rate in treatment group 1.
#' \item \code{stagewiseRates2}: The stage-wise rate in treatment group 2.
#' \item \code{sampleSizesPerStage1}: The stage-wise sample size in treatment group 1.
#' \item \code{sampleSizesPerStage2}: The stage-wise sample size in treatment group 2.
#' \item \code{trialStop}: \code{TRUE} if study should be stopped for efficacy or futility or final stage, \code{FALSE} otherwise.
#' \item \code{conditionalPowerAchieved}: The conditional power for the subsequent stage of the trial for
#' selected sample size and effect. The effect is either estimated from the data or can be
#' user defined with \code{pi1H1} and \code{pi2H1}.
#' }
#'
#' @template return_object_simulation_results
#' @template how_to_get_help_for_generics
#'
#' @template examples_get_simulation_rates
#'
#' @export
#'
getSimulationRates <- function(design = NULL, ...,
groups = 2L,
normalApproximation = TRUE,
riskRatio = FALSE,
thetaH0 = ifelse(riskRatio, 1, 0),
pi1 = seq(0.2, 0.5, 0.1), # C_PI_1_DEFAULT
pi2 = NA_real_,
plannedSubjects = NA_real_,
directionUpper = TRUE, # C_DIRECTION_UPPER_DEFAULT
allocationRatioPlanned = NA_real_,
minNumberOfSubjectsPerStage = NA_real_,
maxNumberOfSubjectsPerStage = NA_real_,
conditionalPower = NA_real_,
pi1H1 = NA_real_,
pi2H1 = NA_real_,
maxNumberOfIterations = 1000L, # C_MAX_SIMULATION_ITERATIONS_DEFAULT
seed = NA_real_,
calcSubjectsFunction = NULL,
showStatistics = FALSE) {
if (is.null(design)) {
design <- .getDefaultDesign(..., type = "simulation")
.warnInCaseOfUnknownArguments(
functionName = "getSimulationRates",
ignore = c(
.getDesignArgumentsToIgnoreAtUnknownArgumentCheck(design, powerCalculationEnabled = TRUE),
"showStatistics"
), ...
)
} else {
.assertIsTrialDesign(design)
.warnInCaseOfUnknownArguments(
functionName = "getSimulationRates",
ignore = c("showStatistics"), ...
)
.warnInCaseOfTwoSidedPowerArgument(...)
}
.assertIsSingleLogical(directionUpper, "directionUpper")
.assertIsSingleNumber(thetaH0, "thetaH0")
.assertIsValidGroupsParameter(groups)
.assertIsSingleLogical(normalApproximation, "normalApproximation")
.assertIsSingleLogical(riskRatio, "riskRatio")
if (groups == 1L) {
.assertIsInOpenInterval(thetaH0, "thetaH0", 0, 1, naAllowed = FALSE)
} else {
if (riskRatio) {
.assertIsInOpenInterval(thetaH0, "thetaH0", 0, NULL, naAllowed = TRUE)
} else {
.assertIsInOpenInterval(thetaH0, "thetaH0", -1, 1, naAllowed = TRUE)
}
}
.assertIsNumericVector(pi1, "pi1", naAllowed = FALSE)
.assertIsInOpenInterval(pi1, "pi1", 0, 1, naAllowed = FALSE)
.assertIsNumericVector(pi2, "pi2", naAllowed = TRUE)
.assertIsInOpenInterval(pi2, "pi2", 0, 1, naAllowed = TRUE)
.assertIsNumericVector(minNumberOfSubjectsPerStage, "minNumberOfSubjectsPerStage", naAllowed = TRUE)
.assertIsNumericVector(maxNumberOfSubjectsPerStage, "maxNumberOfSubjectsPerStage", naAllowed = TRUE)
.assertIsSingleNumber(conditionalPower, "conditionalPower", naAllowed = TRUE)
.assertIsInOpenInterval(conditionalPower, "conditionalPower", 0, 1, naAllowed = TRUE)
.assertIsSingleNumber(pi1H1, "pi1H1", naAllowed = TRUE)
.assertIsInOpenInterval(pi1H1, "pi1H1", 0, 1, naAllowed = TRUE)
.assertIsSingleNumber(pi2H1, "pi2H1", naAllowed = TRUE)
.assertIsInOpenInterval(pi2H1, "pi2H1", 0, 1, naAllowed = TRUE)
.assertIsNumericVector(allocationRatioPlanned, "allocationRatioPlanned", naAllowed = TRUE)
.assertIsInOpenInterval(allocationRatioPlanned, "allocationRatioPlanned", 0, C_ALLOCATION_RATIO_MAXIMUM, naAllowed = TRUE)
.assertIsSinglePositiveInteger(maxNumberOfIterations, "maxNumberOfIterations", validateType = FALSE)
.assertIsSingleNumber(seed, "seed", naAllowed = TRUE)
.assertIsSingleLogical(showStatistics, "showStatistics", naAllowed = FALSE)
.assertIsValidPlannedSubjectsOrEvents(design, plannedSubjects, parameterName = "plannedSubjects")
if (design$sided == 2) {
stop(
C_EXCEPTION_TYPE_ILLEGAL_ARGUMENT,
"only one-sided case is implemented for the simulation design"
)
}
if (!normalApproximation && (groups == 2) && (riskRatio || (thetaH0 != 0))) {
stop(
C_EXCEPTION_TYPE_ILLEGAL_ARGUMENT,
"in the two-sample case, exact test is implemented only for testing H0: pi1 - pi2 = 0"
)
}
simulationResults <- SimulationResultsRates(design, showStatistics = showStatistics)
conditionalPower <- .ignoreParameterIfNotUsed(
"conditionalPower",
conditionalPower, design$kMax > 1, "design is fixed ('kMax' = 1)"
)
minNumberOfSubjectsPerStage <- .ignoreParameterIfNotUsed(
"minNumberOfSubjectsPerStage",
minNumberOfSubjectsPerStage, design$kMax > 1, "design is fixed ('kMax' = 1)"
)
maxNumberOfSubjectsPerStage <- .ignoreParameterIfNotUsed(
"maxNumberOfSubjectsPerStage",
maxNumberOfSubjectsPerStage, design$kMax > 1, "design is fixed ('kMax' = 1)"
)
minNumberOfSubjectsPerStage <- .assertIsValidNumberOfSubjectsPerStage(minNumberOfSubjectsPerStage,
"minNumberOfSubjectsPerStage", plannedSubjects, conditionalPower, calcSubjectsFunction, design$kMax,
endpoint = "rates"
)
maxNumberOfSubjectsPerStage <- .assertIsValidNumberOfSubjectsPerStage(maxNumberOfSubjectsPerStage,
"maxNumberOfSubjectsPerStage", plannedSubjects, conditionalPower, calcSubjectsFunction, design$kMax,
endpoint = "rates"
)
if (design$kMax > 1) {
if (any(maxNumberOfSubjectsPerStage - minNumberOfSubjectsPerStage < 0) &&
!all(is.na(maxNumberOfSubjectsPerStage - minNumberOfSubjectsPerStage))) {
stop(
C_EXCEPTION_TYPE_ILLEGAL_ARGUMENT, "'maxNumberOfSubjectsPerStage' (",
.arrayToString(maxNumberOfSubjectsPerStage),
") must be not smaller than minNumberOfSubjectsPerStage' (",
.arrayToString(minNumberOfSubjectsPerStage), ")"
)
}
.setValueAndParameterType(
simulationResults, "minNumberOfSubjectsPerStage",
minNumberOfSubjectsPerStage, NA_real_
)
.setValueAndParameterType(
simulationResults, "maxNumberOfSubjectsPerStage",
maxNumberOfSubjectsPerStage, NA_real_
)
}
if (design$kMax == 1 && !is.na(conditionalPower)) {
warning("'conditionalPower' will be ignored for fixed sample design", call. = FALSE)
}
if (design$kMax > 1 && is.na(conditionalPower) && is.null(calcSubjectsFunction)) {
if (length(minNumberOfSubjectsPerStage) != 1 ||
!is.na(minNumberOfSubjectsPerStage)) {
warning("'minNumberOfSubjectsPerStage' (",
.arrayToString(minNumberOfSubjectsPerStage), ") ",
"will be ignored because neither 'conditionalPower' nor ",
"'calcSubjectsFunction' is defined",
call. = FALSE
)
simulationResults$minNumberOfSubjectsPerStage <- NA_real_
}
if (length(maxNumberOfSubjectsPerStage) != 1 ||
!is.na(maxNumberOfSubjectsPerStage)) {
warning("'maxNumberOfSubjectsPerStage' (",
.arrayToString(maxNumberOfSubjectsPerStage), ") ",
"will be ignored because neither 'conditionalPower' nor ",
"'calcSubjectsFunction' is defined",
call. = FALSE
)
simulationResults$maxNumberOfSubjectsPerStage <- NA_real_
}
}
pi1H1 <- .ignoreParameterIfNotUsed(
"pi1H1", pi1H1, design$kMax > 1,
"design is fixed ('kMax' = 1)"
)
pi2H1 <- .ignoreParameterIfNotUsed(
"pi2H1", pi2H1, design$kMax > 1,
"design is fixed ('kMax' = 1)"
)
pi1H1 <- .ignoreParameterIfNotUsed("pi1H1", pi1H1, groups == 2, "'groups' = 1")
pi2H1 <- .ignoreParameterIfNotUsed("pi2H1", pi2H1, groups == 2, "'groups' = 1")
.setValueAndParameterType(simulationResults, "pi2", pi2, NA_real_)
if (groups == 1L) {
if (isTRUE(riskRatio)) {
warning("'riskRatio' (", riskRatio, ") will be ignored ",
"because it is not applicable for 'groups' = 1",
call. = FALSE
)
}
if (!is.na(allocationRatioPlanned)) {
warning("'allocationRatioPlanned' (", allocationRatioPlanned,
") will be ignored because it is not applicable for 'groups' = 1",
call. = FALSE
)
simulationResults$allocationRatioPlanned <- NA_real_
}
simulationResults$.setParameterType("allocationRatioPlanned", C_PARAM_NOT_APPLICABLE)
if (!is.na(pi2)) {
warning("'pi2' (", pi2,
") will be ignored because it is not applicable for 'groups' = 1",
call. = FALSE
)
simulationResults$pi2 <- NA_real_
}
simulationResults$.setParameterType("pi2", C_PARAM_NOT_APPLICABLE)
} else {
if (any(is.na(allocationRatioPlanned))) {
allocationRatioPlanned <- C_ALLOCATION_RATIO_DEFAULT
}
if (is.na(pi2)) {
pi2 <- C_PI_2_DEFAULT
simulationResults$pi2 <- pi2
simulationResults$.setParameterType("pi2", C_PARAM_DEFAULT_VALUE)
}
if (length(allocationRatioPlanned) == 1) {
allocationRatioPlanned <- rep(allocationRatioPlanned, design$kMax)
} else if (length(allocationRatioPlanned) != design$kMax) {
stop(
C_EXCEPTION_TYPE_ILLEGAL_ARGUMENT,
"'allocationRatioPlanned' (", .arrayToString(allocationRatioPlanned), ") ",
"must have length 1 or ", design$kMax, " (kMax)"
)
}
if (length(unique(allocationRatioPlanned)) == 1) {
.setValueAndParameterType(
simulationResults, "allocationRatioPlanned",
allocationRatioPlanned[1],
defaultValue = 1
)
} else {
.setValueAndParameterType(
simulationResults, "allocationRatioPlanned",
allocationRatioPlanned,
defaultValue = rep(1, design$kMax)
)
}
}
if (groups == 1) {
effect <- pi1 - thetaH0
} else {
if (riskRatio) {
effect <- pi1 / pi2 - thetaH0
} else {
effect <- pi1 - pi2 - thetaH0
}
}
simulationResults$effect <- effect
simulationResults$.setParameterType(
"effect",
ifelse(groups == 1 && thetaH0 == 0, C_PARAM_NOT_APPLICABLE, C_PARAM_GENERATED)
)
.setValueAndParameterType(simulationResults, "normalApproximation", normalApproximation, TRUE)
.setValueAndParameterType(simulationResults, "riskRatio", riskRatio, FALSE)
.setValueAndParameterType(simulationResults, "thetaH0", thetaH0, ifelse(riskRatio, 1, 0))
.setValueAndParameterType(simulationResults, "pi1", pi1, C_PI_1_DEFAULT)
.setValueAndParameterType(simulationResults, "groups", as.integer(groups), 2L)
.setValueAndParameterType(
simulationResults, "plannedSubjects",
plannedSubjects, NA_real_
)
.setValueAndParameterType(
simulationResults, "directionUpper",
directionUpper, C_DIRECTION_UPPER_DEFAULT
)
.setValueAndParameterType(simulationResults, "minNumberOfSubjectsPerStage",
minNumberOfSubjectsPerStage, NA_real_,
notApplicableIfNA = TRUE
)
.setValueAndParameterType(simulationResults, "maxNumberOfSubjectsPerStage",
maxNumberOfSubjectsPerStage, NA_real_,
notApplicableIfNA = TRUE
)
.setValueAndParameterType(simulationResults, "conditionalPower",
conditionalPower, NA_real_,
notApplicableIfNA = TRUE
)
.setValueAndParameterType(simulationResults, "pi1H1",
pi1H1, NA_real_,
notApplicableIfNA = TRUE
)
.setValueAndParameterType(simulationResults, "pi2H1", pi2H1, 0.2, notApplicableIfNA = TRUE)
.setValueAndParameterType(
simulationResults, "maxNumberOfIterations",
as.integer(maxNumberOfIterations), C_MAX_SIMULATION_ITERATIONS_DEFAULT
)
simulationResults$.setParameterType("seed", ifelse(is.na(seed),
C_PARAM_DEFAULT_VALUE, C_PARAM_USER_DEFINED
))
simulationResults$seed <- .setSeed(seed)
if (.isTrialDesignGroupSequential(design)) {
designNumber <- 1L
} else if (.isTrialDesignInverseNormal(design)) {
designNumber <- 2L
} else if (.isTrialDesignFisher(design)) {
designNumber <- 3L
}
if (.isTrialDesignFisher(design)) {
alpha0Vec <- design$alpha0Vec
futilityBounds <- rep(NA_real_, design$kMax - 1)
} else {
alpha0Vec <- rep(NA_real_, design$kMax - 1)
futilityBounds <- design$futilityBounds
}
calcSubjectsFunctionList <- .getCalcSubjectsFunction(
design = design,
simulationResults = simulationResults,
calcFunction = calcSubjectsFunction,
expectedFunction = .getSimulationRatesStageSubjects
)
calcSubjectsFunctionType <- calcSubjectsFunctionList$calcSubjectsFunctionType
calcSubjectsFunctionR <- calcSubjectsFunctionList$calcSubjectsFunctionR
calcSubjectsFunctionCpp <- calcSubjectsFunctionList$calcSubjectsFunctionCpp
cppResult <- getSimulationRatesCpp(
kMax = design$kMax,
informationRates = design$informationRates,
criticalValues = design$criticalValues,
pi1 = pi1,
pi2 = pi2,
maxNumberOfIterations = maxNumberOfIterations,
designNumber = designNumber,
groups = groups,
futilityBounds = futilityBounds,
alpha0Vec = alpha0Vec,
minNumberOfSubjectsPerStage = minNumberOfSubjectsPerStage,
maxNumberOfSubjectsPerStage = maxNumberOfSubjectsPerStage,
conditionalPower = conditionalPower,
pi1H1 = pi1H1,
pi2H1 = pi2H1,
normalApproximation = normalApproximation,
plannedSubjects = plannedSubjects,
directionUpper = directionUpper,
allocationRatioPlanned = allocationRatioPlanned,
riskRatio = riskRatio,
thetaH0 = thetaH0,
calcSubjectsFunctionType = calcSubjectsFunctionType,
calcSubjectsFunctionR = calcSubjectsFunctionR,
calcSubjectsFunctionCpp = calcSubjectsFunctionCpp
)
data <- cppResult$data
data <- data[!is.na(data$pi1), ]
simulationResults$.data <- data
simulationResults$iterations <- cppResult$iterations
simulationResults$sampleSizes <- cppResult$sampleSizes
simulationResults$rejectPerStage <- cppResult$rejectPerStage
simulationResults$overallReject <- cppResult$overallReject
simulationResults$futilityPerStage <- cppResult$futilityPerStage
simulationResults$futilityStop <- cppResult$futilityStop
simulationResults$earlyStop <- cppResult$earlyStop
simulationResults$expectedNumberOfSubjects <- cppResult$expectedNumberOfSubjects
simulationResults$conditionalPowerAchieved <- cppResult$conditionalPowerAchieved
if (!all(is.na(simulationResults$conditionalPowerAchieved))) {
simulationResults$.setParameterType("conditionalPowerAchieved", C_PARAM_GENERATED)
}
return(simulationResults)
}
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