R/f_simulation_enrichment.R

In rpact: Confirmatory Adaptive Clinical Trial Design and Analysis

## |
## |  *Simulation of enrichment design with 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: 8738 $
## |  Last changed: $Date: 2025-06-04 13:12:30 +0200 (Mi, 04 Jun 2025) $
## |  Last changed by: $Author: wassmer $
## |

#' @include f_simulation_utilities.R
#' @include f_core_utilities.R
NULL

#  Not used in rpact package
.getIndicesOfSelectedSubsets <- function(gMax) {
    subsets <- .getAllAvailableSubsets(1:gMax)
    subsets <- subsets[grepl(as.character(gMax), subsets)]
    indexList <- list()
    subsetIndex <- 1
    if (length(subsets) > 1) {
        subsetIndex <- c(2:length(subsets), 1)
    }
    for (i in subsetIndex) {
        s <- subsets[i]
        indices <- as.integer(strsplit(s, "", fixed = TRUE)[[1]])
        indexList[[length(indexList) + 1]] <- indices
    }
    return(indexList)
}

.createSelectedSubsets <- function(selectedPopulationsAtStagek) {
    gMax <- length(selectedPopulationsAtStagek)

    selectedVector <- rep(FALSE, 2^(gMax - 1))

    if (gMax == 1) {
        selectedVector[1] <- selectedPopulationsAtStagek[1]
    }
    if (gMax == 2) {
        selectedVector[1] <- selectedPopulationsAtStagek[1] || selectedPopulationsAtStagek[2]
        selectedVector[2] <- selectedPopulationsAtStagek[2]
    }
    if (gMax == 3) {
        selectedVector[1] <- selectedPopulationsAtStagek[1] || selectedPopulationsAtStagek[3]
        selectedVector[2] <- selectedPopulationsAtStagek[2] || selectedPopulationsAtStagek[3]
        selectedVector[3] <- selectedPopulationsAtStagek[1] ||
            selectedPopulationsAtStagek[2] ||
            selectedPopulationsAtStagek[3]
        selectedVector[4] <- selectedPopulationsAtStagek[3]
    }
    if (gMax == 4) {
        selectedVector[1] <- selectedPopulationsAtStagek[1] || selectedPopulationsAtStagek[4]
        selectedVector[2] <- selectedPopulationsAtStagek[2] || selectedPopulationsAtStagek[4]
        selectedVector[3] <- selectedPopulationsAtStagek[3] || selectedPopulationsAtStagek[4]
        selectedVector[4] <- selectedPopulationsAtStagek[1] ||
            selectedPopulationsAtStagek[2] ||
            selectedPopulationsAtStagek[4]
        selectedVector[5] <- selectedPopulationsAtStagek[1] ||
            selectedPopulationsAtStagek[3] ||
            selectedPopulationsAtStagek[4]
        selectedVector[6] <- selectedPopulationsAtStagek[2] ||
            selectedPopulationsAtStagek[3] ||
            selectedPopulationsAtStagek[4]
        selectedVector[7] <- selectedPopulationsAtStagek[1] ||
            selectedPopulationsAtStagek[2] ||
            selectedPopulationsAtStagek[3] ||
            selectedPopulationsAtStagek[4]
        selectedVector[8] <- selectedPopulationsAtStagek[4]
    }
    return(selectedVector)
}

.selectPopulations <- function(
    typeOfSelection,
    epsilonValue,
    rValue,
    threshold,
    selectPopulationsFunction,
    selectPopulationsFunctionArgs
) {
    effectVector <- selectPopulationsFunctionArgs$effectVector
    gMax <- length(effectVector)

    if (typeOfSelection != "userDefined") {
        if (typeOfSelection == "all") {
            selectedPopulations <- rep(TRUE, gMax)
        } else {
            selectedPopulations <- rep(FALSE, gMax)
            if (typeOfSelection == "best") {
                selectedPopulations[which.max(effectVector)] <- TRUE
            } else if (tolower(typeOfSelection) == "rbest") {
                selectedPopulations[order(effectVector, decreasing = TRUE)[1:rValue]] <- TRUE
                selectedPopulations[is.na(effectVector)] <- FALSE
            } else if (typeOfSelection == "epsilon") {
                selectedPopulations[max(effectVector, na.rm = TRUE) - effectVector <= epsilonValue] <- TRUE
                selectedPopulations[is.na(effectVector)] <- FALSE
            }
        }
        selectedPopulations[effectVector <= threshold] <- FALSE
    } else {
        functionArgumentNames <- .getFunctionArgumentNames(selectPopulationsFunction, ignoreThreeDots = TRUE)
        .assertIsValidFunction(
            fun = selectPopulationsFunction,
            funArgName = "selectPopulationsFunction",
            expectedArguments = names(selectPopulationsFunctionArgs),
            validateThreeDots = FALSE
        )
        selectedPopulations <- do.call(
            what = selectPopulationsFunction,
            args = selectPopulationsFunctionArgs[functionArgumentNames]
        )
        selectedPopulations[is.na(effectVector)] <- FALSE

        msg <- paste0(
            C_EXCEPTION_TYPE_ILLEGAL_ARGUMENT,
            "'selectPopulationsFunction' returned an illegal or undefined result (",
            .arrayToString(selectedPopulations),
            "); "
        )
        if (length(selectedPopulations) != gMax) {
            stop(msg, "the output must be a logical vector of length 'gMax' (", gMax, ")")
        }
        if (!is.logical(selectedPopulations)) {
            stop(msg, "the output must be a logical vector (is ", .getClassName(selectedPopulations), ")")
        }
    }
    return(selectedPopulations)
}


.performClosedCombinationTestForSimulationEnrichment <- function(
    ...,
    stageResults,
    design,
    indices,
    intersectionTest,
    successCriterion
) {
    if (.isTrialDesignGroupSequential(design) && (design$kMax > 1)) {
        stop(
            C_EXCEPTION_TYPE_ILLEGAL_ARGUMENT,
            "Group sequential design cannot be used for enrichment designs with population selection"
        )
    }

    gMax <- nrow(stageResults$testStatistics)
    kMax <- design$kMax

    adjustedStageWisePValues <- matrix(NA_real_, nrow = 2^gMax - 1, ncol = kMax)
    overallAdjustedTestStatistics <- matrix(NA_real_, nrow = 2^gMax - 1, ncol = kMax)
    rejected <- matrix(FALSE, nrow = gMax, ncol = kMax)
    rejectedIntersections <- matrix(FALSE, nrow = nrow(indices), ncol = kMax)
    futility <- matrix(FALSE, nrow = gMax, ncol = kMax - 1)
    futilityIntersections <- matrix(FALSE, nrow = nrow(indices), ncol = kMax - 1)
    rejectedIntersectionsBefore <- matrix(FALSE, nrow = nrow(indices), ncol = 1)
    successStop <- rep(FALSE, kMax)
    futilityStop <- rep(FALSE, kMax - 1)

    if (.isTrialDesignFisher(design)) {
        weightsFisher <- .getWeightsFisher(design)
    } else {
        weightsInverseNormal <- .getWeightsInverseNormal(design)
    }

    if (gMax == 1) {
        intersectionTest <- "Bonferroni"
    }

    separatePValues <- stageResults$separatePValues

    for (k in 1:kMax) {
        for (i in 1:(2^gMax - 1)) {
            if (!all(is.na(separatePValues[indices[i, ] == 1, k]))) {
                if (intersectionTest == "SpiessensDebois") {
                    if (!is.null(stageResults[["subjectsPerStage"]])) {
                        subjectsSelected <- as.numeric(na.omit(stageResults$subjectsPerStage[
                            indices[i, ] == 1 &
                                stageResults$selectedPopulations[, k],
                            k
                        ]))
                        if (length(subjectsSelected) == 1) {
                            sigma <- 1
                        } else {
                            sigma <- matrix(sqrt(subjectsSelected[1] / sum(subjectsSelected)), nrow = 2, ncol = 2)
                            diag(sigma) <- 1
                        }
                    } else {
                        eventsSelected <- as.numeric(na.omit(stageResults$populationEventsPerStage[
                            indices[i, ] == 1 &
                                stageResults$selectedPopulations[, k],
                            k
                        ]))
                        if (length(eventsSelected) <= 1) {
                            ## eventsSelected can have length = 0!
                            sigma <- 1
                        } else {
                            sigma <- matrix(sqrt(eventsSelected[1] / eventsSelected[2]), nrow = 2, ncol = 2)
                            diag(sigma) <- 1
                        }
                    }
                    maxTestStatistic <- max(stageResults$testStatistics[indices[i, ] == 1, k], na.rm = TRUE)
                    adjustedStageWisePValues[i, k] <- 1 -
                        .getMultivariateDistribution(
                            type = "normal",
                            upper = maxTestStatistic,
                            sigma = sigma,
                            df = NA_real_
                        )
                } else if (intersectionTest == "Bonferroni") {
                    #  Bonferroni adjusted p-values
                    adjustedStageWisePValues[i, k] <- min(c(
                        sum(indices[
                            i,
                            !is.na(separatePValues[, k])
                        ]) *
                            min(separatePValues[indices[i, ] == 1, k], na.rm = TRUE),
                        1
                    ))
                } else if (intersectionTest == "Simes") {
                    #  Simes adjusted p-values
                    adjustedStageWisePValues[i, k] <- min(
                        sum(indices[
                            i,
                            !is.na(separatePValues[, k])
                        ]) /
                            (1:sum(indices[i, !is.na(separatePValues[, k])])) *
                            sort(separatePValues[indices[i, ] == 1, k])
                    )
                } else if (intersectionTest == "Sidak") {
                    #  Sidak adjusted p-values
                    adjustedStageWisePValues[i, k] <- 1 -
                        (1 -
                            min(separatePValues[indices[i, ] == 1, k], na.rm = TRUE))^sum(indices[
                            i,
                            !is.na(separatePValues[, k])
                        ])
                }

                if (.isTrialDesignFisher(design)) {
                    overallAdjustedTestStatistics[i, k] <-
                        prod(adjustedStageWisePValues[i, 1:k]^weightsFisher[1:k])
                } else {
                    overallAdjustedTestStatistics[i, k] <-
                        (weightsInverseNormal[1:k] %*% .getOneMinusQNorm(adjustedStageWisePValues[i, 1:k])) /
                        sqrt(sum(weightsInverseNormal[1:k]^2))
                }
            }

            criticalValues <- .getCriticalValues(design)
            if (.isTrialDesignFisher(design)) {
                rejectedIntersections[i, k] <- (overallAdjustedTestStatistics[i, k] <= criticalValues[k])
                if (k < kMax) {
                    futilityIntersections[i, k] <- (adjustedStageWisePValues[i, k] >= design$alpha0Vec[k])
                }
            } else if (.isTrialDesignInverseNormal(design)) {
                rejectedIntersections[i, k] <- (overallAdjustedTestStatistics[i, k] >= criticalValues[k])
                if (k < kMax) {
                    futilityIntersections[i, k] <- (overallAdjustedTestStatistics[i, k] <= design$futilityBounds[k])
                }
            }

            rejectedIntersections[is.na(rejectedIntersections[, k]), k] <- FALSE

            if (k == kMax && !rejectedIntersections[1, k]) {
                #break
            }
        }

        rejectedIntersections[, k] <- rejectedIntersections[, k] | rejectedIntersectionsBefore
        rejectedIntersectionsBefore <- matrix(rejectedIntersections[, k], ncol = 1)

        for (j in 1:gMax) {
            rejected[j, k] <- all(rejectedIntersections[indices[, j] == 1, k], na.rm = TRUE)
            if (k < kMax) {
                futility[j, k] <- any(futilityIntersections[indices[, j] == 1, k], na.rm = TRUE)
            }
        }

        if (successCriterion == "all") {
            successStop[k] <- all(rejected[stageResults$selectedPopulations[1:gMax, k], k])
        } else {
            successStop[k] <- any(rejected[, k])
        }

        if (k < kMax) {
            futilityStop[k] <- all(futility[stageResults$selectedPopulations[1:gMax, k], k])
            if (all(!stageResults$selectedPopulations[1:gMax, k + 1], na.rm = TRUE)) {
                futilityStop[k] <- TRUE
            }
        }
    }

    return(list(
        separatePValues = separatePValues,
        adjustedStageWisePValues = adjustedStageWisePValues,
        overallAdjustedTestStatistics = overallAdjustedTestStatistics,
        rejected = rejected,
        rejectedIntersections = rejectedIntersections,
        selectedPopulations = stageResults$selectedPopulations,
        successStop = successStop,
        futilityStop = futilityStop
    ))
}

.createSimulationResultsEnrichmentObject <- function(
    ...,
    design,
    effectList,
    kappa = NA_real_, # survival only
    dropoutRate1 = NA_real_, # survival only
    dropoutRate2 = NA_real_, # survival only
    dropoutTime = NA_real_, # survival only
    eventTime = NA_real_, # survival only
    intersectionTest,
    stratifiedAnalysis = NA,
    directionUpper = NA, # rates + survival only
    adaptations,
    typeOfSelection,
    effectMeasure,
    successCriterion,
    epsilonValue,
    rValue,
    threshold,
    plannedSubjects = NA_real_, # means + rates only
    plannedEvents = NA_real_, # survival only
    accrualTime = NA_real_, # survival only
    accrualIntensity = NA_real_, # survival only
    maxNumberOfSubjects = NA_real_, # survival only
    allocationRatioPlanned,
    minNumberOfSubjectsPerStage = NA_real_, # means + rates only
    maxNumberOfSubjectsPerStage = NA_real_, # means + rates only
    minNumberOfEventsPerStage = NA_real_, # survival only
    maxNumberOfEventsPerStage = NA_real_, # survival only
    conditionalPower,
    thetaH1 = NA_real_, # means + survival only
    stDevH1 = NA_real_, # means only
    piTreatmentH1 = NA_real_, # rates only
    piControlH1 = NA_real_, # rates only
    maxNumberOfIterations,
    seed,
    calcSubjectsFunction = NULL, # means + rates only
    calcEventsFunction = NULL, # survival only
    selectPopulationsFunction,
    showStatistics,
    endpoint = c("means", "rates", "survival")
) {
    endpoint <- match.arg(endpoint)

    .assertIsSingleNumber(threshold, "threshold", naAllowed = FALSE)

    .assertIsSingleLogical(stratifiedAnalysis, "stratifiedAnalysis")

    .assertIsSinglePositiveInteger(rValue, "rValue", naAllowed = TRUE, validateType = FALSE)

    .assertIsNumericVector(allocationRatioPlanned, "allocationRatioPlanned", naAllowed = TRUE)
    .assertIsInOpenInterval(
        allocationRatioPlanned,
        "allocationRatioPlanned",
        0,
        C_ALLOCATION_RATIO_MAXIMUM,
        naAllowed = TRUE
    )

    .assertIsSingleNumber(conditionalPower, "conditionalPower", naAllowed = TRUE)
    .assertIsInOpenInterval(conditionalPower, "conditionalPower", 0, 1, naAllowed = TRUE)

    .assertIsLogicalVector(adaptations, "adaptations", naAllowed = TRUE)

    if (endpoint %in% c("means", "rates")) {
        .assertIsNumericVector(minNumberOfSubjectsPerStage, "minNumberOfSubjectsPerStage", naAllowed = TRUE)
        .assertIsNumericVector(maxNumberOfSubjectsPerStage, "maxNumberOfSubjectsPerStage", naAllowed = TRUE)
    } else if (endpoint == "survival") {
        .assertIsNumericVector(minNumberOfEventsPerStage, "minNumberOfEventsPerStage", naAllowed = TRUE)
        .assertIsNumericVector(maxNumberOfEventsPerStage, "maxNumberOfEventsPerStage", naAllowed = TRUE)
    }

    .assertIsSinglePositiveInteger(maxNumberOfIterations, "maxNumberOfIterations", validateType = FALSE)
    .assertIsSingleLogical(showStatistics, "showStatistics", naAllowed = FALSE)
    .assertIsSingleNumber(seed, "seed", naAllowed = TRUE)

    if (endpoint %in% c("rates", "survival")) {
        .assertIsSingleLogical(directionUpper, "directionUpper")
    }

    if (endpoint %in% c("means", "survival")) {
        .assertIsSingleNumber(thetaH1, "thetaH1", naAllowed = TRUE) # means + survival only
    }

    if (endpoint == "means") {
        .assertIsSingleNumber(stDevH1, "stDevH1", naAllowed = TRUE)
        .assertIsInOpenInterval(stDevH1, "stDevH1", 0, NULL, naAllowed = TRUE)
    }

    successCriterion <- .assertIsValidSuccessCriterion(successCriterion)
    effectMeasure <- .assertIsValidEffectMeasure(effectMeasure)

    if (endpoint == "means") {
        simulationResults <- SimulationResultsEnrichmentMeans$new(design, showStatistics = showStatistics)
    } else if (endpoint == "rates") {
        simulationResults <- SimulationResultsEnrichmentRates$new(design, showStatistics = showStatistics)
    } else if (endpoint == "survival") {
        simulationResults <- SimulationResultsEnrichmentSurvival$new(design, showStatistics = showStatistics)
        .setValueAndParameterType(simulationResults, "maxNumberOfSubjects", maxNumberOfSubjects, NA_real_)
    }

    effectList <- .getValidatedEffectList(effectList, endpoint = endpoint)
    gMax <- .getGMaxFromSubGroups(effectList$subGroups)
    if (gMax > 4) {
        stop(C_EXCEPTION_TYPE_ILLEGAL_ARGUMENT, "'populations' (", gMax, ") must not exceed 4")
    }

    .assertIsValidThreshold(threshold, activeArms = gMax)

    intersectionTest <- intersectionTest[1]
    .assertIsValidIntersectionTestEnrichment(design, intersectionTest)

    if (intersectionTest == "SpiessensDebois" && gMax > 2) {
        stop(
            C_EXCEPTION_TYPE_ILLEGAL_ARGUMENT,
            "Spiessen & Debois intersection test cannot generally ",
            "be used for enrichment designs with more than two populations"
        )
    }

    typeOfSelection <- .assertIsValidTypeOfSelection(typeOfSelection, rValue, epsilonValue, gMax)
    if (
        length(typeOfSelection) == 1 &&
            typeOfSelection == "userDefined" &&
            !is.null(threshold) &&
            length(threshold) == 1 &&
            threshold != -Inf
    ) {
        warning(
            "'threshold' (",
            threshold,
            ") will be ignored because 'typeOfSelection' = \"userDefined\"",
            call. = FALSE
        )
        threshold <- -Inf
    }

    if (length(typeOfSelection) == 1 && typeOfSelection != "userDefined" && !is.null(selectPopulationsFunction)) {
        warning(
            "'selectPopulationsFunction' will be ignored because 'typeOfSelection' is not \"userDefined\"",
            call. = FALSE
        )
    } else if (!is.null(selectPopulationsFunction) && is.function(selectPopulationsFunction)) {
        simulationResults$selectPopulationsFunction <- selectPopulationsFunction
    }

    if (endpoint %in% c("rates", "survival")) {
        .setValueAndParameterType(simulationResults, "directionUpper", directionUpper, TRUE)
    }

    if (!stratifiedAnalysis && endpoint %in% c("means")) {
        stop(
            C_EXCEPTION_TYPE_ILLEGAL_ARGUMENT,
            "For testing means, only stratified analysis is supported"
        )
    }

    kMax <- design$kMax
    if (endpoint == "means") {
        stDevH1 <- .ignoreParameterIfNotUsed(
            "stDevH1",
            stDevH1,
            kMax > 1,
            "design is fixed ('kMax' = 1)",
            "Assumed standard deviation"
        )
    } else if (endpoint == "rates") {
        .assertIsSingleNumber(piTreatmentH1, "piTreatmentH1", naAllowed = TRUE)
        .assertIsInOpenInterval(piTreatmentH1, "piTreatmentH1", 0, 1, naAllowed = TRUE)
        piTreatmentH1 <- .ignoreParameterIfNotUsed(
            "piTreatmentH1",
            piTreatmentH1,
            kMax > 1,
            "design is fixed ('kMax' = 1)",
            "Assumed active rate(s)"
        )
        .setValueAndParameterType(simulationResults, "piTreatmentH1", piTreatmentH1, NA_real_)

        .assertIsSingleNumber(piControlH1, "piControlH1", naAllowed = TRUE)
        .assertIsInOpenInterval(piControlH1, "piControlH1", 0, 1, naAllowed = TRUE)
        piControlH1 <- .ignoreParameterIfNotUsed(
            "piControlH1",
            piControlH1,
            kMax > 1,
            "design is fixed ('kMax' = 1)",
            "Assumed control rate(s)"
        )
        .setValueAndParameterType(simulationResults, "piControlH1", piControlH1, NA_real_)
    } else if (endpoint == "survival") {
        .setValueAndParameterType(simulationResults, "eventTime", eventTime, 12)

        if (!is.na(eventTime) && eventTime <= 0) {
            stop(C_EXCEPTION_TYPE_ILLEGAL_ARGUMENT, "'eventTime' (", eventTime, ") must be > 0", call. = FALSE)
        }

        .setValueAndParameterType(simulationResults, "kappa", kappa, 1)

        .setValueAndParameterType(simulationResults, "accrualTime", accrualTime, 12)
        .setValueAndParameterType(simulationResults, "accrualIntensity", accrualIntensity, 0.1)

        .setValueAndParameterType(simulationResults, "dropoutRate1", dropoutRate1, 0)
        .setValueAndParameterType(simulationResults, "dropoutRate2", dropoutRate2, 0)
        .setValueAndParameterType(simulationResults, "dropoutTime", dropoutTime, 12)

        if (!is.na(dropoutTime) && dropoutTime <= 0) {
            stop(C_EXCEPTION_TYPE_ILLEGAL_ARGUMENT, "'dropoutTime' (", dropoutTime, ") must be > 0", call. = FALSE)
        }
        if (!is.na(dropoutRate1) && (dropoutRate1 < 0 || dropoutRate1 >= 1)) {
            stop(
                C_EXCEPTION_TYPE_ARGUMENT_OUT_OF_BOUNDS,
                "'dropoutRate1' (",
                dropoutRate1,
                ") is out of bounds [0; 1)"
            )
        }
        if (!is.na(dropoutRate2) && (dropoutRate2 < 0 || dropoutRate2 >= 1)) {
            stop(
                C_EXCEPTION_TYPE_ARGUMENT_OUT_OF_BOUNDS,
                "'dropoutRate2' (",
                dropoutRate2,
                ") is out of bounds [0; 1)"
            )
        }

        .assertIsIntegerVector(plannedEvents, "plannedEvents", validateType = FALSE)
        if (length(plannedEvents) != kMax) {
            stop(
                C_EXCEPTION_TYPE_ILLEGAL_ARGUMENT,
                "'plannedEvents' (",
                .arrayToString(plannedEvents),
                ") must have length ",
                kMax
            )
        }
        .assertIsInClosedInterval(plannedEvents, "plannedEvents", lower = 1, upper = NULL)
        .assertValuesAreStrictlyIncreasing(plannedEvents, "plannedEvents")
        .setValueAndParameterType(simulationResults, "plannedEvents", plannedEvents, NA_real_)
    }

    if (endpoint %in% c("means", "rates")) {
        .assertIsValidPlannedSubjects(plannedSubjects, kMax) # means + rates only
    }

    if (endpoint %in% c("means", "survival")) {
        thetaH1 <- .ignoreParameterIfNotUsed(
            "thetaH1",
            thetaH1,
            kMax > 1,
            "design is fixed ('kMax' = 1)",
            "Assumed effect"
        )
    }

    conditionalPower <- .ignoreParameterIfNotUsed(
        "conditionalPower",
        conditionalPower,
        kMax > 1,
        "design is fixed ('kMax' = 1)"
    )

    if (endpoint %in% c("means", "rates")) {
        # means + rates only

        minNumberOfSubjectsPerStage <- .ignoreParameterIfNotUsed(
            "minNumberOfSubjectsPerStage",
            minNumberOfSubjectsPerStage,
            kMax > 1,
            "design is fixed ('kMax' = 1)"
        )
        minNumberOfSubjectsPerStage <- .assertIsValidNumberOfSubjectsPerStage(
            minNumberOfSubjectsPerStage,
            "minNumberOfSubjectsPerStage",
            plannedSubjects,
            conditionalPower,
            calcSubjectsFunction,
            kMax,
            endpoint = endpoint
        )

        maxNumberOfSubjectsPerStage <- .ignoreParameterIfNotUsed(
            "maxNumberOfSubjectsPerStage",
            maxNumberOfSubjectsPerStage,
            kMax > 1,
            "design is fixed ('kMax' = 1)"
        )
        maxNumberOfSubjectsPerStage <- .assertIsValidNumberOfSubjectsPerStage(
            maxNumberOfSubjectsPerStage,
            "maxNumberOfSubjectsPerStage",
            plannedSubjects,
            conditionalPower,
            calcSubjectsFunction,
            kMax,
            endpoint = endpoint
        )

        if (kMax > 1) {
            if (
                !all(is.na(maxNumberOfSubjectsPerStage - minNumberOfSubjectsPerStage)) &&
                    any(maxNumberOfSubjectsPerStage - minNumberOfSubjectsPerStage < 0)
            ) {
                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_
            )
        }
    } else if (endpoint == "survival") {
        minNumberOfEventsPerStage <- .ignoreParameterIfNotUsed(
            "minNumberOfEventsPerStage",
            minNumberOfEventsPerStage,
            kMax > 1,
            "design is fixed ('kMax' = 1)"
        )
        minNumberOfEventsPerStage <- .assertIsValidNumberOfSubjectsPerStage(
            minNumberOfEventsPerStage,
            "minNumberOfEventsPerStage",
            plannedEvents,
            conditionalPower,
            calcEventsFunction,
            kMax,
            endpoint = endpoint
        )

        maxNumberOfEventsPerStage <- .ignoreParameterIfNotUsed(
            "maxNumberOfEventsPerStage",
            maxNumberOfEventsPerStage,
            kMax > 1,
            "design is fixed ('kMax' = 1)"
        )
        maxNumberOfEventsPerStage <- .assertIsValidNumberOfSubjectsPerStage(
            maxNumberOfEventsPerStage,
            "maxNumberOfEventsPerStage",
            plannedEvents,
            conditionalPower,
            calcEventsFunction,
            kMax,
            endpoint = endpoint
        )

        if (kMax > 1) {
            if (
                !all(is.na(maxNumberOfEventsPerStage - minNumberOfEventsPerStage)) &&
                    any(maxNumberOfEventsPerStage - minNumberOfEventsPerStage < 0)
            ) {
                stop(
                    C_EXCEPTION_TYPE_ILLEGAL_ARGUMENT,
                    "'maxNumberOfEventsPerStage' (",
                    .arrayToString(maxNumberOfEventsPerStage),
                    ") must be not smaller than 'minNumberOfEventsPerStage' (",
                    .arrayToString(minNumberOfEventsPerStage),
                    ")"
                )
            }
            .setValueAndParameterType(
                simulationResults,
                "minNumberOfEventsPerStage",
                minNumberOfEventsPerStage,
                NA_real_
            )
            .setValueAndParameterType(
                simulationResults,
                "maxNumberOfEventsPerStage",
                maxNumberOfEventsPerStage,
                NA_real_
            )
        }
    }

    if (kMax == 1 && !is.na(conditionalPower)) {
        warning("'conditionalPower' will be ignored for fixed sample design", call. = FALSE)
    }
    if (endpoint %in% c("means", "rates") && kMax == 1 && !is.null(calcSubjectsFunction)) {
        warning("'calcSubjectsFunction' will be ignored for fixed sample design", call. = FALSE)
    }
    if (endpoint == "survival" && kMax == 1 && !is.null(calcEventsFunction)) {
        warning("'calcEventsFunction' will be ignored for fixed sample design", call. = FALSE)
    }

    if (endpoint %in% c("means", "rates") && 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_
        }
    }

    if (endpoint == "survival" && is.na(conditionalPower) && is.null(calcEventsFunction)) {
        if (length(minNumberOfEventsPerStage) != 1 || !is.na(minNumberOfEventsPerStage)) {
            warning(
                "'minNumberOfEventsPerStage' (",
                .arrayToString(minNumberOfEventsPerStage),
                ") ",
                "will be ignored because neither 'conditionalPower' nor 'calcEventsFunction' is defined",
                call. = FALSE
            )
            simulationResults$minNumberOfEventsPerStage <- NA_real_
        }
        if (length(maxNumberOfEventsPerStage) != 1 || !is.na(maxNumberOfEventsPerStage)) {
            warning(
                "'maxNumberOfEventsPerStage' (",
                .arrayToString(maxNumberOfEventsPerStage),
                ") ",
                "will be ignored because neither 'conditionalPower' nor 'calcEventsFunction' is defined",
                call. = FALSE
            )
            simulationResults$maxNumberOfEventsPerStage <- NA_real_
        }
    }

    if (endpoint %in% c("means", "rates")) {
        simulationResults$.setParameterType(
            "calcSubjectsFunction",
            ifelse(
                kMax == 1,
                C_PARAM_NOT_APPLICABLE,
                ifelse(!is.null(calcSubjectsFunction) && kMax > 1, C_PARAM_USER_DEFINED, C_PARAM_DEFAULT_VALUE)
            )
        )
    } else if (endpoint == "survival") {
        simulationResults$.setParameterType(
            "calcEventsFunction",
            ifelse(
                kMax == 1,
                C_PARAM_NOT_APPLICABLE,
                ifelse(!is.null(calcEventsFunction) && kMax > 1, C_PARAM_USER_DEFINED, C_PARAM_DEFAULT_VALUE)
            )
        )
    }

    if (endpoint == "means") {
        if (is.null(calcSubjectsFunction)) {
            calcSubjectsFunction <- .getSimulationMeansEnrichmentStageSubjects
        } else {
            .assertIsValidFunction(
                fun = calcSubjectsFunction,
                funArgName = "calcSubjectsFunction",
                expectedFunction = .getSimulationMeansEnrichmentStageSubjects
            )
        }
        simulationResults$calcSubjectsFunction <- calcSubjectsFunction
    } else if (endpoint == "rates") {
        if (is.null(calcSubjectsFunction)) {
            calcSubjectsFunction <- .getSimulationRatesEnrichmentStageSubjects
        } else {
            .assertIsValidFunction(
                fun = calcSubjectsFunction,
                funArgName = "calcSubjectsFunction",
                expectedFunction = .getSimulationRatesEnrichmentStageSubjects
            )
        }
        simulationResults$calcSubjectsFunction <- calcSubjectsFunction
    } else if (endpoint == "survival") {
        if (is.null(calcEventsFunction)) {
            calcEventsFunction <- .getSimulationSurvivalEnrichmentStageEvents
        } else {
            .assertIsValidFunction(
                fun = calcEventsFunction,
                funArgName = "calcEventsFunction",
                expectedFunction = .getSimulationSurvivalEnrichmentStageEvents
            )
        }
        simulationResults$calcEventsFunction <- calcEventsFunction
    }

    if (any(is.na(allocationRatioPlanned))) {
        allocationRatioPlanned <- C_ALLOCATION_RATIO_DEFAULT
    }

    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 (endpoint %in% c("means", "rates")) {
        .setValueAndParameterType(simulationResults, "plannedSubjects", plannedSubjects, NA_real_)
        .setValueAndParameterType(
            simulationResults,
            "minNumberOfSubjectsPerStage",
            minNumberOfSubjectsPerStage,
            NA_real_,
            notApplicableIfNA = TRUE
        )
        .setValueAndParameterType(
            simulationResults,
            "maxNumberOfSubjectsPerStage",
            maxNumberOfSubjectsPerStage,
            NA_real_,
            notApplicableIfNA = TRUE
        )
    } else if (endpoint == "survival") {
        .setValueAndParameterType(simulationResults, "plannedEvents", plannedEvents, NA_real_)
        .setValueAndParameterType(
            simulationResults,
            "minNumberOfEventsPerStage",
            minNumberOfEventsPerStage,
            NA_real_,
            notApplicableIfNA = TRUE
        )
        .setValueAndParameterType(
            simulationResults,
            "maxNumberOfEventsPerStage",
            maxNumberOfEventsPerStage,
            NA_real_,
            notApplicableIfNA = TRUE
        )
    }
    .setValueAndParameterType(
        simulationResults,
        "conditionalPower",
        conditionalPower,
        NA_real_,
        notApplicableIfNA = TRUE
    )
    if (endpoint %in% c("means", "survival")) {
        .setValueAndParameterType(simulationResults, "thetaH1", thetaH1, NA_real_, notApplicableIfNA = TRUE)
    }
    if (endpoint == "means") {
        .setValueAndParameterType(simulationResults, "stDevH1", stDevH1, NA_real_, 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 (is.null(adaptations) || all(is.na(adaptations))) {
        adaptations <- rep(TRUE, kMax - 1)
    }
    if (length(adaptations) != kMax - 1) {
        stop(C_EXCEPTION_TYPE_ILLEGAL_ARGUMENT, "'adaptations' must have length ", (kMax - 1), " (kMax - 1)")
    }
    .setValueAndParameterType(simulationResults, "adaptations", adaptations, rep(TRUE, kMax - 1))

    simulationResults$effectList <- effectList
    simulationResults$.setParameterType("effectList", C_PARAM_USER_DEFINED)

    simulationResults$populations <- as.integer(gMax)
    simulationResults$.setParameterType("populations", C_PARAM_DERIVED)

    .setValueAndParameterType(
        simulationResults,
        "stratifiedAnalysis",
        stratifiedAnalysis,
        C_STRATIFIED_ANALYSIS_DEFAULT
    )

    if (typeOfSelection != "userDefined") {
        .setValueAndParameterType(simulationResults, "threshold", threshold, -Inf)
        .setValueAndParameterType(simulationResults, "epsilonValue", epsilonValue, NA_real_)
        .setValueAndParameterType(simulationResults, "rValue", rValue, NA_real_)
    }
    .setValueAndParameterType(
        simulationResults,
        "intersectionTest",
        intersectionTest,
        C_INTERSECTION_TEST_ENRICHMENT_DEFAULT
    )
    .setValueAndParameterType(simulationResults, "typeOfSelection", typeOfSelection, C_TYPE_OF_SELECTION_DEFAULT)
    .setValueAndParameterType(simulationResults, "successCriterion", successCriterion, C_SUCCESS_CRITERION_DEFAULT)
    .setValueAndParameterType(simulationResults, "effectMeasure", effectMeasure, C_EFFECT_MEASURE_DEFAULT)

    return(simulationResults)
}