Nothing
## |
## | *Analysis of survival in enrichment designs with adaptive 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 $
## |
## |
#' @include f_logger.R
NULL
#'
#' @title
#' Get Analysis Results Survival
#'
#' @description
#' Returns an analysis result object.
#'
#' @param design The trial design.
#'
#' @return Returns a \code{AnalysisResultsSurvival} object.
#'
#' @keywords internal
#'
#' @noRd
#'
.calcSurvivalTestStatistics <- function(dataInput, subset, stage, thetaH0,
stratifiedAnalysis, directionUpper = TRUE) {
overallEvents <- NA_real_
testStatistics <- NA_real_
separatePValues <- NA_real_
overallAllocationRatios <- NA_real_
overallTestStatistics <- NA_real_
if (!all(is.na(dataInput$getOverallEvents(stage = stage, subset = subset)))) {
overallEvents <- sum(dataInput$getOverallEvents(stage = stage, subset = subset), na.rm = TRUE)
if (dataInput$isStratified()) {
overallAllocationRatios <- sum(dataInput$getOverallAllocationRatios(stage = stage, subset = subset) *
dataInput$getOverallEvents(stage = stage, subset = subset), na.rm = TRUE) /
sum(dataInput$getOverallEvents(stage = stage, subset = subset), na.rm = TRUE)
overallTestStatistics <- (sum(dataInput$getOverallEvents(stage = stage, subset = subset), na.rm = TRUE) -
sum(dataInput$getOverallExpectedEvents(stage = stage, subset = subset), na.rm = TRUE)) /
sqrt(sum(dataInput$getOverallVarianceEvents(stage = stage, subset = subset), na.rm = TRUE)) -
sqrt(sum(dataInput$getOverallEvents(stage = stage, subset = subset), na.rm = TRUE)) *
sqrt(overallAllocationRatios) / (1 + overallAllocationRatios) * log(thetaH0)
if (stage == 1) {
testStatistics <- overallTestStatistics
} else {
testStatistics <- (sqrt(sum(dataInput$getOverallEvents(stage = stage, subset = subset), na.rm = TRUE)) *
(sum(dataInput$getOverallEvents(stage = stage, subset = subset), na.rm = TRUE) -
sum(dataInput$getOverallExpectedEvents(stage = stage, subset = subset), na.rm = TRUE)) /
sqrt(sum(dataInput$getOverallVarianceEvents(stage = stage, subset = subset), na.rm = TRUE)) -
sqrt(sum(dataInput$getOverallEvents(stage = stage - 1, subset = subset), na.rm = TRUE)) *
(sum(dataInput$getOverallEvents(stage = stage - 1, subset = subset) -
dataInput$getOverallExpectedEvents(stage = stage - 1, subset = subset), na.rm = TRUE)) /
sqrt(sum(dataInput$getOverallVarianceEvents(stage = stage - 1, subset = subset), na.rm = TRUE))) /
sqrt(sum(dataInput$getOverallEvents(stage = stage, subset = subset) -
dataInput$getOverallEvents(stage = stage - 1, subset = subset), na.rm = TRUE)) -
sqrt(sum(dataInput$getOverallEvents(stage = stage, subset = subset) -
dataInput$getOverallEvents(stage = stage - 1, subset = subset), na.rm = TRUE)) *
sqrt(overallAllocationRatios) / (1 + overallAllocationRatios) * log(thetaH0)
}
}
# non-stratified data input
else {
overallTestStatistics <- dataInput$getOverallLogRanks(stage = stage, subset = subset) -
sqrt(dataInput$getOverallEvents(stage = stage, subset = subset)) *
sqrt(dataInput$getOverallAllocationRatios(stage = stage, subset = subset)) /
(1 + dataInput$getOverallAllocationRatios(stage = stage, subset = subset)) * log(thetaH0)
testStatistics <- dataInput$getLogRanks(stage = stage, subset = subset) -
sqrt(dataInput$getEvents(stage = stage, subset = subset)) *
sqrt(dataInput$getAllocationRatios(stage = stage, subset = subset)) /
(1 + dataInput$getAllocationRatios(stage = stage, subset = subset)) * log(thetaH0)
overallAllocationRatios <- dataInput$getOverallAllocationRatios(stage = stage, subset = subset)
}
if (directionUpper) {
separatePValues <- 1 - stats::pnorm(testStatistics)
} else {
separatePValues <- stats::pnorm(testStatistics)
}
}
if (("R" %in% subset) && is.na(dataInput$getOverallEvents(stage = stage, subset = "R")) ||
("S1" %in% subset) && is.na(dataInput$getOverallEvents(stage = stage, subset = "S1")) ||
("S2" %in% subset) && is.na(dataInput$getOverallEvents(stage = stage, subset = "S2")) ||
("S3" %in% subset) && is.na(dataInput$getOverallEvents(stage = stage, subset = "S3")) ||
("S4" %in% subset) && is.na(dataInput$getOverallEvents(stage = stage, subset = "S4"))
) {
overallEvents <- NA_real_
separatePValues <- NA_real_
testStatistics <- NA_real_
overallAllocationRatios <- NA_real_
overallTestStatistics <- NA_real_
}
return(list(
overallEvents = overallEvents,
separatePValues = separatePValues,
testStatistics = testStatistics,
overallAllocationRatios = overallAllocationRatios,
overallTestStatistics = overallTestStatistics
))
}
.getStageResultsSurvivalEnrichment <- function(..., design, dataInput,
thetaH0 = C_THETA_H0_SURVIVAL_DEFAULT,
directionUpper = C_DIRECTION_UPPER_DEFAULT,
stratifiedAnalysis = C_STRATIFIED_ANALYSIS_DEFAULT,
intersectionTest = C_INTERSECTION_TEST_ENRICHMENT_DEFAULT,
calculateSingleStepAdjusted = FALSE,
userFunctionCallEnabled = FALSE) {
.assertIsTrialDesign(design)
.assertIsDatasetSurvival(dataInput)
.assertIsValidThetaH0DataInput(thetaH0, dataInput)
.assertIsValidDirectionUpper(directionUpper, design$sided)
.assertIsSingleLogical(calculateSingleStepAdjusted, "calculateSingleStepAdjusted")
.warnInCaseOfUnknownArguments(
functionName = ".getStageResultsSurvivalEnrichment",
ignore = c(.getDesignArgumentsToIgnoreAtUnknownArgumentCheck(
design,
powerCalculationEnabled = TRUE
), "stage"), ...
)
stage <- .getStageFromOptionalArguments(..., dataInput = dataInput, design = design)
kMax <- design$kMax
if (dataInput$isStratified()) {
gMax <- log(length(levels(factor(dataInput$subsets))), 2) + 1
} else {
gMax <- length(levels(factor(dataInput$subsets)))
}
.assertIsValidIntersectionTestEnrichment(design, intersectionTest)
if (gMax > 2 && intersectionTest == "SpiessensDebois") {
stop(
C_EXCEPTION_TYPE_ILLEGAL_ARGUMENT, "gMax (", gMax,
") > 2: Spiessens & Debois intersection test test can only be used for one subset"
)
}
if (!stratifiedAnalysis) {
stop(
C_EXCEPTION_TYPE_ILLEGAL_ARGUMENT,
"only stratified analysis can be performed for enrichment survival designs"
)
}
if (dataInput$isStratified() && gMax > 4) {
stop(
C_EXCEPTION_TYPE_ILLEGAL_ARGUMENT, "gMax (", gMax,
") > 4: Stratified analysis not implemented"
)
}
stageResults <- StageResultsEnrichmentSurvival(
design = design,
dataInput = dataInput,
intersectionTest = intersectionTest,
thetaH0 = thetaH0,
direction = ifelse(directionUpper, C_DIRECTION_UPPER, C_DIRECTION_LOWER),
directionUpper = directionUpper,
stratifiedAnalysis = stratifiedAnalysis,
stage = stage
)
.setValueAndParameterType(
stageResults, "stratifiedAnalysis",
stratifiedAnalysis, C_STRATIFIED_ANALYSIS_DEFAULT
)
.setValueAndParameterType(
stageResults, "intersectionTest",
intersectionTest, C_INTERSECTION_TEST_ENRICHMENT_DEFAULT
)
effectSizes <- matrix(NA_real_, nrow = gMax, ncol = kMax)
testStatistics <- matrix(NA_real_, nrow = gMax, ncol = kMax)
separatePValues <- matrix(NA_real_, nrow = gMax, ncol = kMax)
overallTestStatistics <- matrix(NA_real_, nrow = gMax, ncol = kMax)
overallEvents <- matrix(NA_real_, nrow = gMax, ncol = kMax)
dimnames(testStatistics) <- list(paste("population ", 1:gMax, sep = ""), paste("stage ", (1:kMax), sep = ""))
dimnames(separatePValues) <- list(paste("population ", 1:gMax, sep = ""), paste("stage ", (1:kMax), sep = ""))
subsets <- .createSubsetsByGMax(gMax = gMax, stratifiedInput = dataInput$isStratified(), subsetIdPrefix = "S")
for (k in 1:stage) {
for (population in 1:gMax) {
subset <- subsets[[population]]
results <- .calcSurvivalTestStatistics(
dataInput, subset, k,
thetaH0, stratifiedAnalysis, directionUpper
)
effectSizes[population, k] <- thetaH0 * exp(results$overallTestStatistics *
(1 + results$overallAllocationRatios) /
sqrt(results$overallAllocationRatios * results$overallEvents))
overallTestStatistics[population, k] <- results$overallTestStatistics
testStatistics[population, k] <- results$testStatistics
separatePValues[population, k] <- results$separatePValues
overallEvents[population, k] <- results$overallEvents
}
}
.setWeightsToStageResults(design, stageResults)
# calculation of single stage adjusted p-Values and overall test statistics for determination of RCIs
if (calculateSingleStepAdjusted) {
singleStepAdjustedPValues <- matrix(NA_real_, nrow = gMax, ncol = kMax)
combInverseNormal <- matrix(NA_real_, nrow = gMax, ncol = kMax)
combFisher <- matrix(NA_real_, nrow = gMax, ncol = kMax)
if (.isTrialDesignInverseNormal(design)) {
weightsInverseNormal <- stageResults$weightsInverseNormal
} else if (.isTrialDesignFisher(design)) {
weightsFisher <- stageResults$weightsFisher
}
for (k in 1:stage) {
selected <- sum(!is.na(separatePValues[, k]))
for (population in 1:gMax) {
if ((intersectionTest == "Bonferroni") || (intersectionTest == "Simes")) {
singleStepAdjustedPValues[population, k] <- min(1, separatePValues[population, k] * selected)
} else if (intersectionTest == "Sidak") {
singleStepAdjustedPValues[population, k] <- 1 - (1 - separatePValues[population, k])^selected
} else if (intersectionTest == "SpiessensDebois") {
if (!is.na(testStatistics[population, k])) {
df <- NA_real_
sigma <- 1
if (selected == 2) {
if (dataInput$isStratified()) {
sigma <- matrix(rep(sqrt(dataInput$getEvents(stage = k, subset = "S1") /
sum(dataInput$getEvents(stage = k))), 4), nrow = 2)
} else {
sigma <- matrix(rep(sqrt(dataInput$getEvents(stage = k, subset = "S1") /
dataInput$getEvents(stage = k, subset = "F")), 4), nrow = 2)
}
diag(sigma) <- 1
}
singleStepAdjustedPValues[population, k] <- 1 - .getMultivariateDistribution(
type = "normal",
upper = ifelse(directionUpper, testStatistics[population, k], -testStatistics[population, k]),
sigma = sigma, df = NA
)
}
}
if (.isTrialDesignInverseNormal(design)) {
combInverseNormal[population, k] <- (weightsInverseNormal[1:k] %*%
.getOneMinusQNorm(singleStepAdjustedPValues[population, 1:k])) /
sqrt(sum(weightsInverseNormal[1:k]^2))
} else if (.isTrialDesignFisher(design)) {
combFisher[population, k] <- prod(singleStepAdjustedPValues[population, 1:k]^weightsFisher[1:k])
}
}
}
stageResults$overallTestStatistics <- overallTestStatistics
stageResults$effectSizes <- effectSizes
stageResults$testStatistics <- testStatistics
stageResults$separatePValues <- separatePValues
stageResults$singleStepAdjustedPValues <- singleStepAdjustedPValues
stageResults$.setParameterType("singleStepAdjustedPValues", C_PARAM_GENERATED)
if (.isTrialDesignFisher(design)) {
stageResults$combFisher <- combFisher
stageResults$.setParameterType("combFisher", C_PARAM_GENERATED)
} else if (.isTrialDesignInverseNormal(design)) {
stageResults$combInverseNormal <- combInverseNormal
stageResults$.setParameterType("combInverseNormal", C_PARAM_GENERATED)
}
} else {
stageResults$overallTestStatistics <- overallTestStatistics
stageResults$.overallEvents <- overallEvents
stageResults$effectSizes <- effectSizes
stageResults$testStatistics <- testStatistics
stageResults$separatePValues <- separatePValues
}
return(stageResults)
}
.getAnalysisResultsSurvivalEnrichment <- function(..., design, dataInput) {
if (.isTrialDesignInverseNormal(design)) {
return(.getAnalysisResultsSurvivalInverseNormalEnrichment(
design = design, dataInput = dataInput, ...
))
}
if (.isTrialDesignFisher(design)) {
return(.getAnalysisResultsSurvivalFisherEnrichment(
design = design, dataInput = dataInput, ...
))
}
.stopWithWrongDesignMessageEnrichment(design)
}
.getAnalysisResultsSurvivalInverseNormalEnrichment <- function(...,
design, dataInput,
intersectionTest = C_INTERSECTION_TEST_ENRICHMENT_DEFAULT,
directionUpper = C_DIRECTION_UPPER_DEFAULT,
stratifiedAnalysis = C_STRATIFIED_ANALYSIS_DEFAULT,
thetaH0 = C_THETA_H0_SURVIVAL_DEFAULT,
thetaH1 = NA_real_,
nPlanned = NA_real_,
allocationRatioPlanned = C_ALLOCATION_RATIO_DEFAULT,
tolerance = C_ANALYSIS_TOLERANCE_DEFAULT) {
.assertIsTrialDesignInverseNormal(design)
stage <- .getStageFromOptionalArguments(..., dataInput = dataInput, design = design)
.warnInCaseOfUnknownArguments(
functionName = ".getAnalysisResultsSurvivalInverseNormalEnrichment",
ignore = c(.getDesignArgumentsToIgnoreAtUnknownArgumentCheck(
design,
powerCalculationEnabled = TRUE
), "stage"), ...
)
results <- AnalysisResultsEnrichmentInverseNormal(design = design, dataInput = dataInput)
results <- .getAnalysisResultsSurvivalEnrichmentAll(
results = results, design = design, dataInput = dataInput,
intersectionTest = intersectionTest, stage = stage, directionUpper = directionUpper,
stratifiedAnalysis = stratifiedAnalysis,
thetaH0 = thetaH0, thetaH1 = thetaH1, nPlanned = nPlanned,
allocationRatioPlanned = allocationRatioPlanned,
tolerance = tolerance
)
return(results)
}
.getAnalysisResultsSurvivalFisherEnrichment <- function(...,
design, dataInput,
intersectionTest = C_INTERSECTION_TEST_ENRICHMENT_DEFAULT,
directionUpper = C_DIRECTION_UPPER_DEFAULT,
stratifiedAnalysis = C_STRATIFIED_ANALYSIS_DEFAULT,
thetaH0 = C_THETA_H0_SURVIVAL_DEFAULT,
thetaH1 = NA_real_,
nPlanned = NA_real_,
allocationRatioPlanned = C_ALLOCATION_RATIO_DEFAULT,
tolerance = C_ANALYSIS_TOLERANCE_DEFAULT,
iterations = C_ITERATIONS_DEFAULT, seed = NA_real_) {
.assertIsTrialDesignFisher(design)
.assertIsValidIterationsAndSeed(iterations, seed, zeroIterationsAllowed = FALSE)
stage <- .getStageFromOptionalArguments(..., dataInput = dataInput, design = design)
.warnInCaseOfUnknownArguments(
functionName = ".getAnalysisResultsSurvivalFisherEnrichment",
ignore = c(.getDesignArgumentsToIgnoreAtUnknownArgumentCheck(
design,
powerCalculationEnabled = TRUE
), "stage"), ...
)
results <- AnalysisResultsEnrichmentFisher(design = design, dataInput = dataInput)
.setValueAndParameterType(results, "iterations", as.integer(iterations), C_ITERATIONS_DEFAULT)
.setValueAndParameterType(results, "seed", seed, NA_real_)
results <- .getAnalysisResultsSurvivalEnrichmentAll(
results = results, design = design, dataInput = dataInput,
intersectionTest = intersectionTest, stage = stage, directionUpper = directionUpper,
stratifiedAnalysis = stratifiedAnalysis,
thetaH0 = thetaH0, thetaH1 = thetaH1, nPlanned = nPlanned,
allocationRatioPlanned = allocationRatioPlanned,
tolerance = tolerance,
iterations = iterations, seed = seed
)
return(results)
}
.getAnalysisResultsSurvivalEnrichmentAll <- function(..., results,
design, dataInput, intersectionTest, stage,
directionUpper, stratifiedAnalysis, thetaH0, thetaH1, nPlanned,
allocationRatioPlanned, tolerance, iterations, seed) {
startTime <- Sys.time()
stageResults <- .getStageResultsSurvivalEnrichment(
design = design, dataInput = dataInput,
intersectionTest = intersectionTest, stage = stage,
thetaH0 = thetaH0, directionUpper = directionUpper,
stratifiedAnalysis = stratifiedAnalysis
)
results$.setStageResults(stageResults)
.logProgress("Stage results calculated", startTime = startTime)
thetaH1 <- .assertIsValidThetaH1ForEnrichment(thetaH1, stageResults, stage, results = results)
.setValueAndParameterType(results, "intersectionTest", intersectionTest, C_INTERSECTION_TEST_ENRICHMENT_DEFAULT)
.setValueAndParameterType(results, "directionUpper", directionUpper, C_DIRECTION_UPPER_DEFAULT)
.setValueAndParameterType(results, "stratifiedAnalysis", stratifiedAnalysis, C_STRATIFIED_ANALYSIS_DEFAULT)
.setValueAndParameterType(results, "thetaH0", thetaH0, C_THETA_H0_MEANS_DEFAULT)
.setConditionalPowerArguments(results, dataInput, nPlanned, allocationRatioPlanned)
.setNPlannedAndThetaH1(results, nPlanned, thetaH1)
startTime <- Sys.time()
results$.closedTestResults <- getClosedCombinationTestResults(stageResults = stageResults)
.logProgress("Closed test calculated", startTime = startTime)
if (design$kMax > 1) {
# conditional power
startTime <- Sys.time()
if (.isTrialDesignFisher(design)) {
results$.conditionalPowerResults <- .getConditionalPowerSurvivalEnrichment(
stageResults = stageResults,
stage = stage, nPlanned = nPlanned, allocationRatioPlanned = allocationRatioPlanned,
thetaH1 = thetaH1, iterations = iterations, seed = seed
)
.synchronizeIterationsAndSeed(results)
} else {
results$.conditionalPowerResults <- .getConditionalPowerSurvivalEnrichment(
stageResults = stageResults,
stage = stage, nPlanned = nPlanned, allocationRatioPlanned = allocationRatioPlanned,
thetaH1 = thetaH1
)
results$conditionalPower <- results$.conditionalPowerResults$conditionalPower
results$.setParameterType("conditionalPower", C_PARAM_GENERATED)
}
results$thetaH1 <- matrix(results$.conditionalPowerResults$thetaH1, ncol = 1)
.logProgress("Conditional power calculated", startTime = startTime)
# CRP - conditional rejection probabilities
startTime <- Sys.time()
results$conditionalRejectionProbabilities <- .getConditionalRejectionProbabilitiesEnrichment(
stageResults = stageResults, stage = stage, iterations = iterations, seed = seed
)
results$.setParameterType("conditionalRejectionProbabilities", C_PARAM_GENERATED)
.logProgress("Conditional rejection probabilities (CRP) calculated", startTime = startTime)
} else {
results$.setParameterType("conditionalPower", C_PARAM_NOT_APPLICABLE)
results$.setParameterType("conditionalPowerSimulated", C_PARAM_NOT_APPLICABLE)
results$.setParameterType("conditionalRejectionProbabilities", C_PARAM_NOT_APPLICABLE)
}
# RCI - repeated confidence interval
repeatedConfidenceIntervalLowerBounds <- numeric(0)
repeatedConfidenceIntervalUpperBounds <- numeric(0)
startTime <- Sys.time()
repeatedConfidenceIntervals <- .getRepeatedConfidenceIntervalsSurvivalEnrichment(
design = design, dataInput = dataInput,
stratifiedAnalysis = stratifiedAnalysis,
intersectionTest = intersectionTest,
stage = stage,
tolerance = tolerance
)
gMax <- stageResults$getGMax()
results$repeatedConfidenceIntervalLowerBounds <-
matrix(rep(NA_real_, gMax * design$kMax), nrow = gMax, ncol = design$kMax)
results$repeatedConfidenceIntervalUpperBounds <- results$repeatedConfidenceIntervalLowerBounds
for (k in 1:design$kMax) {
for (population in 1:gMax) {
results$repeatedConfidenceIntervalLowerBounds[population, k] <-
repeatedConfidenceIntervals[population, 1, k]
results$repeatedConfidenceIntervalUpperBounds[population, k] <-
repeatedConfidenceIntervals[population, 2, k]
}
}
results$.setParameterType("repeatedConfidenceIntervalLowerBounds", C_PARAM_GENERATED)
results$.setParameterType("repeatedConfidenceIntervalUpperBounds", C_PARAM_GENERATED)
# repeated p-value
results$repeatedPValues <- .getRepeatedPValuesEnrichment(stageResults = stageResults, tolerance = tolerance)
results$.setParameterType("repeatedPValues", C_PARAM_GENERATED)
message("Test statistics from full (and sub-populations) need to be stratified log-rank tests")
return(results)
}
.getRootThetaSurvivalEnrichment <- function(..., design, dataInput, treatmentArm, stage,
directionUpper, stratifiedAnalysis, intersectionTest, thetaLow, thetaUp,
firstParameterName, secondValue, tolerance) {
result <- .getOneDimensionalRoot(
function(theta) {
stageResults <- .getStageResultsSurvivalEnrichment(
design = design, dataInput = dataInput,
stage = stage, thetaH0 = theta, directionUpper = directionUpper,
stratifiedAnalysis = stratifiedAnalysis,
intersectionTest = intersectionTest, calculateSingleStepAdjusted = TRUE
)
firstValue <- stageResults[[firstParameterName]][treatmentArm, stage]
return(firstValue - secondValue)
},
lower = thetaLow, upper = thetaUp, tolerance = tolerance,
callingFunctionInformation = ".getRootThetaSurvivalEnrichment"
)
return(result)
}
.getUpperLowerThetaSurvivalEnrichment <- function(...,
design, dataInput, theta, treatmentArm, stage,
directionUpper, conditionFunction, stratifiedAnalysis,
intersectionTest, firstParameterName, secondValue) {
stageResults <- .getStageResultsSurvivalEnrichment(
design = design, dataInput = dataInput,
stage = stage, thetaH0 = exp(theta), directionUpper = directionUpper,
stratifiedAnalysis = stratifiedAnalysis,
intersectionTest = intersectionTest, calculateSingleStepAdjusted = TRUE
)
firstValue <- stageResults[[firstParameterName]][treatmentArm, stage]
maxSearchIterations <- 30
while (conditionFunction(secondValue, firstValue)) {
theta <- 2 * theta
stageResults <- .getStageResultsSurvivalEnrichment(
design = design, dataInput = dataInput,
stage = stage, thetaH0 = exp(theta), directionUpper = directionUpper,
stratifiedAnalysis = stratifiedAnalysis,
intersectionTest = intersectionTest, calculateSingleStepAdjusted = TRUE
)
firstValue <- stageResults[[firstParameterName]][treatmentArm, stage]
maxSearchIterations <- maxSearchIterations - 1
if (maxSearchIterations < 0) {
stop(
C_EXCEPTION_TYPE_RUNTIME_ISSUE,
sprintf(
paste0(
"failed to find theta (k = %s, firstValue = %s, ",
"secondValue = %s, levels(firstValue) = %s, theta = %s)"
),
stage, stageResults[[firstParameterName]][treatmentArm, stage], secondValue,
firstValue, theta
)
)
}
}
return(theta)
}
.getRepeatedConfidenceIntervalsSurvivalEnrichmentAll <- function(...,
design, dataInput,
directionUpper = C_DIRECTION_UPPER_DEFAULT,
stratifiedAnalysis = C_STRATIFIED_ANALYSIS_DEFAULT,
intersectionTest = C_INTERSECTION_TEST_ENRICHMENT_DEFAULT,
tolerance = C_ANALYSIS_TOLERANCE_DEFAULT,
firstParameterName) {
.assertIsValidIntersectionTestEnrichment(design, intersectionTest)
stage <- .getStageFromOptionalArguments(..., dataInput = dataInput, design = design)
stageResults <- .getStageResultsSurvivalEnrichment(
design = design, dataInput = dataInput,
stage = stage, thetaH0 = 1, directionUpper = directionUpper,
stratifiedAnalysis = stratifiedAnalysis,
intersectionTest = intersectionTest, calculateSingleStepAdjusted = FALSE
)
gMax <- stageResults$getGMax()
repeatedConfidenceIntervals <- array(NA_real_, dim = c(gMax, 2, design$kMax))
# Repeated onfidence intervals when using combination tests
if (.isTrialDesignFisher(design)) {
bounds <- design$alpha0Vec
border <- C_ALPHA_0_VEC_DEFAULT
criticalValues <- design$criticalValues
conditionFunction <- .isFirstValueSmallerThanSecondValue
} else if (.isTrialDesignInverseNormal(design)) {
bounds <- design$futilityBounds
border <- C_FUTILITY_BOUNDS_DEFAULT
criticalValues <- design$criticalValues
criticalValues[is.infinite(criticalValues) & criticalValues > 0] <- C_QNORM_MAXIMUM
criticalValues[is.infinite(criticalValues) & criticalValues < 0] <- C_QNORM_MINIMUM
conditionFunction <- .isFirstValueGreaterThanSecondValue
}
if (any(is.na(criticalValues[1:stage]))) {
warning("Repeated confidence intervals not because ", sum(is.na(criticalValues)),
" critical values are NA (", .arrayToString(criticalValues), ")",
call. = FALSE
)
return(repeatedConfidenceIntervals)
}
# necessary for adjustment for binding futility boundaries
futilityCorr <- rep(NA_real_, design$kMax)
stages <- (1:stage)
for (k in stages) {
startTime <- Sys.time()
for (population in 1:gMax) {
if (!is.na(stageResults$testStatistics[population, k]) && criticalValues[k] < C_QNORM_MAXIMUM) {
# Finding maximum upper and minimum lower bounds for RCIs
thetaLow <- exp(.getUpperLowerThetaSurvivalEnrichment(
design = design, dataInput = dataInput,
theta = -1, treatmentArm = population, stage = k, directionUpper = TRUE,
stratifiedAnalysis = stratifiedAnalysis, intersectionTest = intersectionTest,
conditionFunction = conditionFunction, firstParameterName = firstParameterName,
secondValue = criticalValues[k]
))
thetaUp <- exp(.getUpperLowerThetaSurvivalEnrichment(
design = design, dataInput = dataInput,
theta = 1, treatmentArm = population, stage = k, directionUpper = FALSE,
stratifiedAnalysis = stratifiedAnalysis, intersectionTest = intersectionTest,
conditionFunction = conditionFunction, firstParameterName = firstParameterName,
secondValue = criticalValues[k]
))
# finding upper and lower RCI limits through root function
repeatedConfidenceIntervals[population, 1, k] <- .getRootThetaSurvivalEnrichment(
design = design,
dataInput = dataInput, treatmentArm = population, stage = k, directionUpper = TRUE,
thetaLow = thetaLow, thetaUp = thetaUp, stratifiedAnalysis = stratifiedAnalysis,
intersectionTest = intersectionTest, firstParameterName = firstParameterName,
secondValue = criticalValues[k], tolerance = tolerance
)
repeatedConfidenceIntervals[population, 2, k] <- .getRootThetaSurvivalEnrichment(
design = design,
dataInput = dataInput, treatmentArm = population, stage = k, directionUpper = FALSE,
thetaLow = thetaLow, thetaUp = thetaUp, stratifiedAnalysis = stratifiedAnalysis,
intersectionTest = intersectionTest, firstParameterName = firstParameterName,
secondValue = criticalValues[k], tolerance = tolerance
)
# adjustment for binding futility bounds
if (k > 1 && !is.na(bounds[k - 1]) && conditionFunction(bounds[k - 1], border) && design$bindingFutility) {
parameterName <- ifelse(.isTrialDesignFisher(design),
"singleStepAdjustedPValues", firstParameterName
)
# Calculate new lower and upper bounds
if (directionUpper) {
thetaLow <- tolerance
} else {
thetaUp <- .getUpperLowerThetaSurvivalEnrichment(
design = design,
dataInput = dataInput,
theta = 1, treatmentArm = population, stage = k - 1, directionUpper = FALSE,
conditionFunction = conditionFunction, stratifiedAnalysis = stratifiedAnalysis,
intersectionTest = intersectionTest, firstParameterName = parameterName,
secondValue = bounds[k - 1]
)
}
futilityCorr[k] <- .getRootThetaSurvivalEnrichment(
design = design, dataInput = dataInput,
treatmentArm = population, stage = k - 1, directionUpper = directionUpper,
thetaLow = thetaLow, thetaUp = thetaUp, stratifiedAnalysis = stratifiedAnalysis,
intersectionTest = intersectionTest, firstParameterName = parameterName,
secondValue = bounds[k - 1], tolerance = tolerance
)
if (directionUpper) {
repeatedConfidenceIntervals[population, 1, k] <- min(
min(futilityCorr[2:k]),
repeatedConfidenceIntervals[population, 1, k]
)
} else {
repeatedConfidenceIntervals[population, 2, k] <- max(
max(futilityCorr[2:k]),
repeatedConfidenceIntervals[population, 2, k]
)
}
}
if (!is.na(repeatedConfidenceIntervals[population, 1, k]) &&
!is.na(repeatedConfidenceIntervals[population, 2, k]) &&
repeatedConfidenceIntervals[population, 1, k] > repeatedConfidenceIntervals[population, 2, k]) {
repeatedConfidenceIntervals[population, , k] <- rep(NA_real_, 2)
}
}
}
.logProgress("Repeated confidence intervals for stage %s calculated", startTime = startTime, k)
}
return(repeatedConfidenceIntervals)
}
#'
#' RCIs based on inverse normal combination test
#'
#' @noRd
#'
.getRepeatedConfidenceIntervalsSurvivalEnrichmentInverseNormal <- function(...,
design, dataInput,
directionUpper = C_DIRECTION_UPPER_DEFAULT,
stratifiedAnalysis = C_STRATIFIED_ANALYSIS_DEFAULT,
intersectionTest = C_INTERSECTION_TEST_ENRICHMENT_DEFAULT,
tolerance = C_ANALYSIS_TOLERANCE_DEFAULT) {
.warnInCaseOfUnknownArguments(
functionName =
".getRepeatedConfidenceIntervalsSurvivalEnrichmentInverseNormal",
ignore = c(.getDesignArgumentsToIgnoreAtUnknownArgumentCheck(
design,
powerCalculationEnabled = TRUE
), "stage"), ...
)
return(.getRepeatedConfidenceIntervalsSurvivalEnrichmentAll(
design = design, dataInput = dataInput,
directionUpper = directionUpper,
stratifiedAnalysis = stratifiedAnalysis, intersectionTest = intersectionTest,
tolerance = tolerance, firstParameterName = "combInverseNormal", ...
))
}
#'
#' RCIs based on Fisher's combination test
#'
#' @noRd
#'
.getRepeatedConfidenceIntervalsSurvivalEnrichmentFisher <- function(...,
design, dataInput,
directionUpper = C_DIRECTION_UPPER_DEFAULT,
stratifiedAnalysis = C_STRATIFIED_ANALYSIS_DEFAULT,
intersectionTest = C_INTERSECTION_TEST_ENRICHMENT_DEFAULT,
tolerance = C_ANALYSIS_TOLERANCE_DEFAULT) {
.warnInCaseOfUnknownArguments(
functionName =
".getRepeatedConfidenceIntervalsSurvivalEnrichmentFisher",
ignore = c(.getDesignArgumentsToIgnoreAtUnknownArgumentCheck(
design,
powerCalculationEnabled = TRUE
), "stage"), ...
)
return(.getRepeatedConfidenceIntervalsSurvivalEnrichmentAll(
design = design, dataInput = dataInput,
directionUpper = directionUpper,
stratifiedAnalysis = stratifiedAnalysis, intersectionTest = intersectionTest,
tolerance = tolerance, firstParameterName = "combFisher", ...
))
}
#'
#' Calculation of lower and upper limits of repeated confidence intervals (RCIs) for Survival
#'
#' @noRd
#'
.getRepeatedConfidenceIntervalsSurvivalEnrichment <- function(..., design) {
if (.isTrialDesignInverseNormal(design)) {
return(.getRepeatedConfidenceIntervalsSurvivalEnrichmentInverseNormal(design = design, ...))
}
if (.isTrialDesignFisher(design)) {
return(.getRepeatedConfidenceIntervalsSurvivalEnrichmentFisher(design = design, ...))
}
.stopWithWrongDesignMessageEnrichment(design)
}
#'
#' Calculation of conditional power for Survival
#'
#' @noRd
#'
.getConditionalPowerSurvivalEnrichment <- function(..., stageResults, stage = stageResults$stage,
nPlanned, allocationRatioPlanned = C_ALLOCATION_RATIO_DEFAULT,
thetaH1 = NA_real_,
iterations = C_ITERATIONS_DEFAULT, seed = NA_real_) {
design <- stageResults$.design
gMax <- stageResults$getGMax()
kMax <- design$kMax
results <- ConditionalPowerResultsEnrichmentSurvival(
.design = design,
.stageResults = stageResults,
thetaH1 = thetaH1,
nPlanned = nPlanned,
allocationRatioPlanned = allocationRatioPlanned
)
if (any(is.na(nPlanned))) {
return(results)
}
.assertIsValidStage(stage, kMax)
if (stage == kMax) {
.logDebug(
"Conditional power will be calculated only for subsequent stages ",
"(stage = ", stage, ", kMax = ", kMax, ")"
)
return(results)
}
if (!.isValidNPlanned(nPlanned = nPlanned, kMax = kMax, stage = stage)) {
return(results)
}
.assertIsValidNPlanned(nPlanned, kMax, stage)
.assertIsSingleNumber(allocationRatioPlanned, "allocationRatioPlanned")
.assertIsInOpenInterval(allocationRatioPlanned, "allocationRatioPlanned", 0, C_ALLOCATION_RATIO_MAXIMUM)
results$.setParameterType("nPlanned", C_PARAM_USER_DEFINED)
results$.setParameterType(
"allocationRatioPlanned",
ifelse(allocationRatioPlanned == C_ALLOCATION_RATIO_DEFAULT, C_PARAM_DEFAULT_VALUE, C_PARAM_USER_DEFINED)
)
thetaH1 <- .assertIsValidThetaH1ForEnrichment(thetaH1, stageResults, stage, results = results)
if (any(thetaH1 <= 0, na.rm = TRUE)) {
stop(C_EXCEPTION_TYPE_ILLEGAL_ARGUMENT, "'thetaH1' (", thetaH1, ") must be > 0")
}
if ((length(thetaH1) != 1) && (length(thetaH1) != gMax)) {
stop(
C_EXCEPTION_TYPE_ILLEGAL_ARGUMENT,
sprintf(paste0(
"length of 'thetaH1' (%s) must be ",
"equal to 'gMax' (%s) or 1"
), .arrayToString(thetaH1), gMax)
)
}
if (.isTrialDesignInverseNormal(design)) {
return(.getConditionalPowerSurvivalEnrichmentInverseNormal(
results = results,
design = design, stageResults = stageResults, stage = stage,
nPlanned = nPlanned, allocationRatioPlanned = allocationRatioPlanned,
thetaH1 = thetaH1, ...
))
} else if (.isTrialDesignFisher(design)) {
return(.getConditionalPowerSurvivalEnrichmentFisher(
results = results,
design = design, stageResults = stageResults, stage = stage,
nPlanned = nPlanned, allocationRatioPlanned = allocationRatioPlanned,
thetaH1 = thetaH1,
iterations = iterations, seed = seed, ...
))
}
stop(
C_EXCEPTION_TYPE_ILLEGAL_ARGUMENT,
"'design' must be an instance of TrialDesignInverseNormal or TrialDesignFisher"
)
}
#'
#' Calculation of conditional power based on inverse normal method
#'
#' @noRd
#'
.getConditionalPowerSurvivalEnrichmentInverseNormal <- function(..., results, design, stageResults, stage,
allocationRatioPlanned, nPlanned, thetaH1) {
.assertIsTrialDesignInverseNormal(design)
.warnInCaseOfUnknownArguments(functionName = ".getConditionalPowerSurvivalEnrichmentInverseNormal", ...)
kMax <- design$kMax
gMax <- stageResults$getGMax()
weights <- .getWeightsInverseNormal(design)
informationRates <- design$informationRates
nPlanned <- c(rep(NA_real_, stage), nPlanned)
nPlanned <- allocationRatioPlanned / (1 + allocationRatioPlanned)^2 * nPlanned
.setValueAndParameterType(results, "allocationRatioPlanned", allocationRatioPlanned, C_ALLOCATION_RATIO_DEFAULT)
if (length(thetaH1) == 1) {
thetaH1 <- rep(thetaH1, gMax)
results$.setParameterType("thetaH1", C_PARAM_GENERATED)
} else {
results$.setParameterType("thetaH1", C_PARAM_DEFAULT_VALUE)
}
if (stageResults$directionUpper) {
standardizedEffect <- log(thetaH1 / stageResults$thetaH0)
} else {
standardizedEffect <- -log(thetaH1 / stageResults$thetaH0)
}
ctr <- .performClosedCombinationTest(stageResults = stageResults)
criticalValues <- design$criticalValues
for (population in 1:gMax) {
if (!is.na(ctr$separatePValues[population, stage])) {
# shifted decision region for use in getGroupSeqProbs
# Inverse Normal Method
shiftedDecisionRegionUpper <- criticalValues[(stage + 1):kMax] *
sqrt(sum(weights[1:stage]^2) + cumsum(weights[(stage + 1):kMax]^2)) /
sqrt(cumsum(weights[(stage + 1):kMax]^2)) -
min(ctr$overallAdjustedTestStatistics[ctr$indices[, population] == 1, stage], na.rm = TRUE) *
sqrt(sum(weights[1:stage]^2)) /
sqrt(cumsum(weights[(stage + 1):kMax]^2)) - standardizedEffect[population] *
cumsum(sqrt(nPlanned[(stage + 1):kMax]) * weights[(stage + 1):kMax]) /
sqrt(cumsum(weights[(stage + 1):kMax]^2))
if (stage == kMax - 1) {
shiftedFutilityBounds <- c()
} else {
shiftedFutilityBounds <- design$futilityBounds[(stage + 1):(kMax - 1)] *
sqrt(sum(weights[1:stage]^2) + cumsum(weights[(stage + 1):(kMax - 1)]^2)) /
sqrt(cumsum(weights[(stage + 1):(kMax - 1)]^2)) -
min(ctr$overallAdjustedTestStatistics[ctr$indices[, population] == 1, stage], na.rm = TRUE) *
sqrt(sum(weights[1:stage]^2)) /
sqrt(cumsum(weights[(stage + 1):(kMax - 1)]^2)) - standardizedEffect[population] *
cumsum(sqrt(nPlanned[(stage + 1):(kMax - 1)]) * weights[(stage + 1):(kMax - 1)]) /
sqrt(cumsum(weights[(stage + 1):(kMax - 1)]^2))
}
# scaled information for use in getGroupSeqProbs
scaledInformation <- (informationRates[(stage + 1):kMax] - informationRates[stage]) /
(1 - informationRates[stage])
decisionMatrix <- matrix(c(
shiftedFutilityBounds, C_FUTILITY_BOUNDS_DEFAULT,
shiftedDecisionRegionUpper
), nrow = 2, byrow = TRUE)
probs <- .getGroupSequentialProbabilities(
decisionMatrix = decisionMatrix,
informationRates = scaledInformation
)
results$conditionalPower[population, (stage + 1):kMax] <- cumsum(probs[3, ] - probs[2, ])
}
}
nPlanned <- (1 + allocationRatioPlanned)^2 / allocationRatioPlanned * nPlanned
results$nPlanned <- nPlanned
results$.setParameterType("nPlanned", C_PARAM_GENERATED)
results$.setParameterType("conditionalPower", C_PARAM_GENERATED)
results$thetaH1 <- thetaH1
return(results)
}
#'
#' Calculation of conditional power based on Fisher's combination test
#'
#' @noRd
#'
.getConditionalPowerSurvivalEnrichmentFisher <- function(..., results, design, stageResults, stage,
allocationRatioPlanned, nPlanned, thetaH1, iterations, seed) {
.assertIsTrialDesignFisher(design)
.assertIsValidIterationsAndSeed(iterations, seed, zeroIterationsAllowed = FALSE)
.warnInCaseOfUnknownArguments(functionName = ".getConditionalPowerSurvivalEnrichmentFisher", ...)
kMax <- design$kMax
gMax <- stageResults$getGMax()
criticalValues <- design$criticalValues
weightsFisher <- .getWeightsFisher(design)
results$iterations <- as.integer(iterations)
results$.setParameterType("iterations", C_PARAM_USER_DEFINED)
results$.setParameterType("seed", ifelse(is.na(seed), C_PARAM_DEFAULT_VALUE, C_PARAM_USER_DEFINED))
results$seed <- .setSeed(seed)
results$simulated <- FALSE
results$.setParameterType("simulated", C_PARAM_DEFAULT_VALUE)
.setValueAndParameterType(results, "allocationRatioPlanned", allocationRatioPlanned, C_ALLOCATION_RATIO_DEFAULT)
if (length(thetaH1) == 1) {
thetaH1 <- rep(thetaH1, gMax)
results$.setParameterType("thetaH1", C_PARAM_GENERATED)
} else {
results$.setParameterType("thetaH1", C_PARAM_DEFAULT_VALUE)
}
if (stageResults$directionUpper) {
standardizedEffect <- log(thetaH1 / stageResults$thetaH0)
} else {
standardizedEffect <- -log(thetaH1 / stageResults$thetaH0)
}
nPlanned <- c(rep(NA_real_, stage), nPlanned)
nPlanned <- allocationRatioPlanned / (1 + allocationRatioPlanned)^2 * nPlanned
ctr <- .performClosedCombinationTest(stageResults = stageResults)
for (population in 1:gMax) {
if (!is.na(ctr$separatePValues[population, stage])) {
if (gMax == 1) {
pValues <- ctr$adjustedStageWisePValues[ctr$indices[, population] == 1, ][1:stage]
} else {
pValues <- ctr$adjustedStageWisePValues[ctr$indices[, population] == 1, ][which.max(
ctr$overallAdjustedTestStatistics[ctr$indices[, population] == 1, stage]
), 1:stage]
}
if (stage < kMax - 1) {
for (k in (stage + 1):kMax) {
reject <- 0
for (i in 1:iterations) {
reject <- reject + .getRejectValueConditionalPowerFisher(
kMax = kMax, alpha0Vec = design$alpha0Vec,
criticalValues = criticalValues, weightsFisher = weightsFisher,
pValues = pValues, currentKMax = k, thetaH1 = standardizedEffect[population],
stage = stage, nPlanned = nPlanned
)
}
results$conditionalPower[population, k] <- reject / iterations
}
results$simulated <- TRUE
results$.setParameterType("simulated", C_PARAM_GENERATED)
} else if (stage == kMax - 1) {
divisor <- prod(pValues[1:(kMax - 1)]^weightsFisher[1:(kMax - 1)])
result <- 1 - (criticalValues[kMax] / divisor)^(1 / weightsFisher[kMax])
if (result <= 0 || result >= 1) {
warning("Calculation not possible: could not calculate conditional power for stage ", kMax, call. = FALSE)
results$conditionalPower[population, kMax] <- NA_real_
} else {
results$conditionalPower[population, kMax] <- 1 - stats::pnorm(.getQNorm(result) -
standardizedEffect[population] * sqrt(nPlanned[kMax]))
}
}
}
}
nPlanned <- (1 + allocationRatioPlanned)^2 / allocationRatioPlanned * nPlanned
results$nPlanned <- nPlanned
results$.setParameterType("nPlanned", C_PARAM_GENERATED)
results$.setParameterType("conditionalPower", C_PARAM_GENERATED)
results$thetaH1 <- thetaH1
return(results)
}
#'
#' Calculation of conditional power and likelihood values for plotting the graph
#'
#' @noRd
#'
.getConditionalPowerLikelihoodSurvivalEnrichment <- function(..., stageResults, stage,
nPlanned, allocationRatioPlanned = C_ALLOCATION_RATIO_DEFAULT,
thetaRange, iterations = C_ITERATIONS_DEFAULT, seed = NA_real_) {
.assertIsSingleNumber(allocationRatioPlanned, "allocationRatioPlanned")
.assertIsInOpenInterval(allocationRatioPlanned, "allocationRatioPlanned", 0, C_ALLOCATION_RATIO_MAXIMUM)
.associatedArgumentsAreDefined(nPlanned = nPlanned, thetaRange = thetaRange)
design <- stageResults$.design
kMax <- design$kMax
gMax <- stageResults$getGMax()
intersectionTest <- stageResults$intersectionTest
thetaRange <- .assertIsValidThetaH1ForEnrichment(thetaH1 = thetaRange)
if (length(thetaRange) == 1) {
stop(
C_EXCEPTION_TYPE_ILLEGAL_ARGUMENT,
"length of 'thetaRange' (", .arrayToString(thetaRange), ") must be at least 2"
)
}
populations <- numeric(gMax * length(thetaRange))
effectValues <- numeric(gMax * length(thetaRange))
condPowerValues <- numeric(gMax * length(thetaRange))
likelihoodValues <- numeric(gMax * length(thetaRange))
stdErr <- 2 / sqrt(stageResults$.overallEvents[, stage])
results <- ConditionalPowerResultsEnrichmentSurvival(
.design = design,
.stageResults = stageResults,
nPlanned = nPlanned,
allocationRatioPlanned = allocationRatioPlanned
)
j <- 1
for (i in seq(along = thetaRange)) {
for (population in (1:gMax)) {
populations[j] <- population
effectValues[j] <- thetaRange[i]
if (.isTrialDesignInverseNormal(design)) {
condPowerValues[j] <- .getConditionalPowerSurvivalEnrichmentInverseNormal(
results = results,
design = design, stageResults = stageResults, stage = stage, nPlanned = nPlanned,
allocationRatioPlanned = allocationRatioPlanned,
thetaH1 = thetaRange[i], ...
)$conditionalPower[population, kMax]
} else if (.isTrialDesignFisher(design)) {
condPowerValues[j] <- .getConditionalPowerSurvivalEnrichmentFisher(
results = results,
design = design, stageResults = stageResults, stage = stage, nPlanned = nPlanned,
allocationRatioPlanned = allocationRatioPlanned,
thetaH1 = thetaRange[i],
iterations = iterations, seed = seed, ...
)$conditionalPower[population, kMax]
}
likelihoodValues[j] <- stats::dnorm(
log(thetaRange[i]), log(stageResults$effectSizes[population, stage]),
stdErr[population]
) / stats::dnorm(0, 0, stdErr[population])
j <- j + 1
}
}
subtitle <- paste0(
"Intersection test = ", intersectionTest,
", Stage = ", stage, ", # of remaining events = ", sum(nPlanned),
", allocation ratio = ", .formatSubTitleValue(allocationRatioPlanned, "allocationRatioPlanned")
)
return(list(
populations = populations,
xValues = effectValues,
condPowerValues = condPowerValues,
likelihoodValues = likelihoodValues,
main = C_PLOT_MAIN_CONDITIONAL_POWER_WITH_LIKELIHOOD,
xlab = "Hazard ratio",
ylab = C_PLOT_YLAB_CONDITIONAL_POWER_WITH_LIKELIHOOD,
sub = subtitle
))
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.