Nothing
## |
## | *Analysis of means in multi-arm 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: 7206 $
## | Last changed: $Date: 2023-07-25 14:55:05 +0200 (Tue, 25 Jul 2023) $
## | Last changed by: $Author: pahlke $
## |
#' @include f_logger.R
NULL
.getAnalysisResultsMeansMultiArm <- function(..., design, dataInput) {
if (.isTrialDesignInverseNormal(design)) {
return(.getAnalysisResultsMeansInverseNormalMultiArm(design = design, dataInput = dataInput, ...))
}
if (.isTrialDesignFisher(design)) {
return(.getAnalysisResultsMeansFisherMultiArm(design = design, dataInput = dataInput, ...))
}
if (.isTrialDesignConditionalDunnett(design)) {
return(.getAnalysisResultsMeansConditionalDunnettMultiArm(design = design, dataInput = dataInput, ...))
}
.stopWithWrongDesignMessage(design)
}
.getAnalysisResultsMeansInverseNormalMultiArm <- function(...,
design, dataInput,
intersectionTest = C_INTERSECTION_TEST_MULTIARMED_DEFAULT,
directionUpper = C_DIRECTION_UPPER_DEFAULT,
normalApproximation = C_NORMAL_APPROXIMATION_MEANS_DEFAULT,
varianceOption = C_VARIANCE_OPTION_MULTIARMED_DEFAULT,
thetaH0 = C_THETA_H0_MEANS_DEFAULT,
thetaH1 = NA_real_, assumedStDevs = NA_real_,
nPlanned = NA_real_,
allocationRatioPlanned = C_ALLOCATION_RATIO_DEFAULT,
calculateSingleStepAdjusted = FALSE,
tolerance = C_ANALYSIS_TOLERANCE_DEFAULT) {
.assertIsTrialDesignInverseNormal(design)
stage <- .getStageFromOptionalArguments(..., dataInput = dataInput, design = design)
.warnInCaseOfUnknownArguments(
functionName = ".getAnalysisResultsMeansInverseNormalMultiArm",
ignore = c(.getDesignArgumentsToIgnoreAtUnknownArgumentCheck(
design,
powerCalculationEnabled = TRUE
), "stage"), ...
)
results <- AnalysisResultsMultiArmInverseNormal(design = design, dataInput = dataInput)
results <- .getAnalysisResultsMeansMultiArmAll(
results = results, design = design, dataInput = dataInput,
intersectionTest = intersectionTest, stage = stage, directionUpper = directionUpper,
normalApproximation = normalApproximation, varianceOption = varianceOption,
thetaH0 = thetaH0, thetaH1 = thetaH1, assumedStDevs = assumedStDevs, nPlanned = nPlanned,
allocationRatioPlanned = allocationRatioPlanned,
calculateSingleStepAdjusted = calculateSingleStepAdjusted, tolerance = tolerance
)
return(results)
}
.getAnalysisResultsMeansFisherMultiArm <- function(...,
design, dataInput,
intersectionTest = C_INTERSECTION_TEST_MULTIARMED_DEFAULT,
directionUpper = C_DIRECTION_UPPER_DEFAULT,
normalApproximation = C_NORMAL_APPROXIMATION_MEANS_DEFAULT,
varianceOption = C_VARIANCE_OPTION_MULTIARMED_DEFAULT,
thetaH0 = C_THETA_H0_MEANS_DEFAULT,
thetaH1 = NA_real_, assumedStDevs = NA_real_,
nPlanned = NA_real_,
allocationRatioPlanned = C_ALLOCATION_RATIO_DEFAULT,
calculateSingleStepAdjusted = FALSE,
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 = ".getAnalysisResultsMeansFisherMultiArm",
ignore = c(.getDesignArgumentsToIgnoreAtUnknownArgumentCheck(
design,
powerCalculationEnabled = TRUE
), "stage"), ...
)
results <- AnalysisResultsMultiArmFisher(design = design, dataInput = dataInput)
results <- .getAnalysisResultsMeansMultiArmAll(
results = results, design = design, dataInput = dataInput,
intersectionTest = intersectionTest, stage = stage, directionUpper = directionUpper,
normalApproximation = normalApproximation, varianceOption = varianceOption,
thetaH0 = thetaH0, thetaH1 = thetaH1, assumedStDevs = assumedStDevs, nPlanned = nPlanned,
allocationRatioPlanned = allocationRatioPlanned,
calculateSingleStepAdjusted = calculateSingleStepAdjusted,
tolerance = tolerance, iterations = iterations, seed = seed
)
return(results)
}
.getAnalysisResultsMeansConditionalDunnettMultiArm <- function(...,
design, dataInput,
intersectionTest = C_INTERSECTION_TEST_MULTIARMED_DEFAULT,
directionUpper = C_DIRECTION_UPPER_DEFAULT,
normalApproximation = C_NORMAL_APPROXIMATION_MEANS_DEFAULT,
varianceOption = C_VARIANCE_OPTION_MULTIARMED_DEFAULT,
thetaH0 = C_THETA_H0_MEANS_DEFAULT,
thetaH1 = NA_real_, assumedStDevs = NA_real_,
nPlanned = NA_real_,
allocationRatioPlanned = C_ALLOCATION_RATIO_DEFAULT,
calculateSingleStepAdjusted = FALSE,
tolerance = C_ANALYSIS_TOLERANCE_DEFAULT) {
.assertIsTrialDesignConditionalDunnett(design)
stage <- .getStageFromOptionalArguments(..., dataInput = dataInput, design = design)
.warnInCaseOfUnknownArguments(
functionName = ".getAnalysisResultsMeansConditionalDunnettMultiArm",
ignore = c(.getDesignArgumentsToIgnoreAtUnknownArgumentCheck(
design,
powerCalculationEnabled = TRUE
), "stage"), ...
)
results <- AnalysisResultsConditionalDunnett(design = design, dataInput = dataInput)
results <- .getAnalysisResultsMeansMultiArmAll(
results = results, design = design,
dataInput = dataInput, intersectionTest = intersectionTest,
stage = stage, directionUpper = directionUpper, normalApproximation = normalApproximation,
varianceOption = varianceOption,
thetaH0 = thetaH0, thetaH1 = thetaH1, assumedStDevs = assumedStDevs, nPlanned = nPlanned,
allocationRatioPlanned = allocationRatioPlanned,
calculateSingleStepAdjusted = calculateSingleStepAdjusted,
tolerance = tolerance,
iterations = iterations, seed = seed
)
return(results)
}
.getAnalysisResultsMeansMultiArmAll <- function(..., results, design, dataInput, intersectionTest, stage,
directionUpper, normalApproximation, varianceOption, thetaH0, thetaH1, assumedStDevs,
nPlanned, allocationRatioPlanned, calculateSingleStepAdjusted, tolerance,
iterations, seed) {
startTime <- Sys.time()
intersectionTest <- .getCorrectedIntersectionTestMultiArmIfNecessary(design, intersectionTest)
stageResults <- .getStageResultsMeansMultiArm(
design = design, dataInput = dataInput,
intersectionTest = intersectionTest, stage = stage,
thetaH0 = thetaH0, directionUpper = directionUpper,
normalApproximation = normalApproximation, varianceOption = varianceOption,
calculateSingleStepAdjusted = calculateSingleStepAdjusted,
userFunctionCallEnabled = TRUE
)
normalApproximation <- stageResults$normalApproximation
intersectionTest <- stageResults$intersectionTest
results$.setStageResults(stageResults)
.logProgress("Stage results calculated", startTime = startTime)
numberOfGroups <- dataInput$getNumberOfGroups()
thetaH1 <- .assertIsValidThetaH1ForMultiArm(thetaH1, stageResults, stage, results = results)
assumedStDevs <- .assertIsValidAssumedStDevForMultiHypotheses(
assumedStDevs, stageResults, stage,
results = results
)
.setValueAndParameterType(
results, "intersectionTest",
intersectionTest, C_INTERSECTION_TEST_MULTIARMED_DEFAULT
)
.setValueAndParameterType(
results, "directionUpper",
directionUpper, C_DIRECTION_UPPER_DEFAULT
)
.setValueAndParameterType(
results, "normalApproximation",
normalApproximation, C_NORMAL_APPROXIMATION_MEANS_DEFAULT
)
.setValueAndParameterType(
results, "varianceOption",
varianceOption, C_VARIANCE_OPTION_MULTIARMED_DEFAULT
)
.setValueAndParameterType(results, "thetaH0", thetaH0, C_THETA_H0_MEANS_DEFAULT)
.setConditionalPowerArguments(results, dataInput, nPlanned, allocationRatioPlanned)
.setNPlannedAndThetaH1AndAssumedStDevs(results, nPlanned, thetaH1, assumedStDevs)
startTime <- Sys.time()
if (!.isTrialDesignConditionalDunnett(design)) {
results$.closedTestResults <- getClosedCombinationTestResults(stageResults = stageResults)
} else {
results$.closedTestResults <- getClosedConditionalDunnettTestResults(
stageResults = stageResults, design = design, stage = stage
)
}
.logProgress("Closed test calculated", startTime = startTime)
results$.setParameterType("seed", C_PARAM_NOT_APPLICABLE)
results$.setParameterType("iterations", C_PARAM_NOT_APPLICABLE)
if (design$kMax > 1) {
# conditional power
startTime <- Sys.time()
if (.isTrialDesignFisher(design)) {
results$.conditionalPowerResults <- .getConditionalPowerMeansMultiArm(
stageResults = stageResults,
stage = stage, nPlanned = nPlanned, allocationRatioPlanned = allocationRatioPlanned,
thetaH1 = thetaH1, assumedStDevs = assumedStDevs, iterations = iterations, seed = seed
)
.synchronizeIterationsAndSeed(results)
} else {
results$.conditionalPowerResults <- .getConditionalPowerMeansMultiArm(
stageResults = stageResults,
stage = stage, nPlanned = nPlanned, allocationRatioPlanned = allocationRatioPlanned,
thetaH1 = thetaH1, assumedStDevs = assumedStDevs
)
results$conditionalPower <- results$.conditionalPowerResults$conditionalPower
results$.setParameterType("conditionalPower", C_PARAM_GENERATED)
}
results$thetaH1 <- matrix(results$.conditionalPowerResults$thetaH1, ncol = 1)
results$assumedStDevs <- matrix(results$.conditionalPowerResults$assumedStDevs, ncol = 1)
.logProgress("Conditional power calculated", startTime = startTime)
# CRP - conditional rejection probabilities
startTime <- Sys.time()
results$conditionalRejectionProbabilities <- .getConditionalRejectionProbabilitiesMultiArm(
stageResults = stageResults, stage = stage
)
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
repeatedConfidenceIntervals <- .getRepeatedConfidenceIntervalsMeansMultiArm(
design = design, dataInput = dataInput,
intersectionTest = intersectionTest, stage = stage,
normalApproximation = normalApproximation,
varianceOption = varianceOption,
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$kMax) {
for (treatmentArm in 1:gMax) {
results$repeatedConfidenceIntervalLowerBounds[treatmentArm, k] <-
repeatedConfidenceIntervals[treatmentArm, 1, k]
results$repeatedConfidenceIntervalUpperBounds[treatmentArm, k] <-
repeatedConfidenceIntervals[treatmentArm, 2, k]
}
}
results$.setParameterType("repeatedConfidenceIntervalLowerBounds", C_PARAM_GENERATED)
results$.setParameterType("repeatedConfidenceIntervalUpperBounds", C_PARAM_GENERATED)
# repeated p-value
results$repeatedPValues <- .getRepeatedPValuesMultiArm(
stageResults = stageResults, tolerance = tolerance
)
results$.setParameterType("repeatedPValues", C_PARAM_GENERATED)
return(results)
}
.getStageResultsMeansMultiArm <- function(..., design, dataInput,
thetaH0 = C_THETA_H0_MEANS_DEFAULT,
directionUpper = C_DIRECTION_UPPER_DEFAULT,
normalApproximation = C_NORMAL_APPROXIMATION_MEANS_DEFAULT,
varianceOption = C_VARIANCE_OPTION_MULTIARMED_DEFAULT,
intersectionTest = C_INTERSECTION_TEST_MULTIARMED_DEFAULT,
calculateSingleStepAdjusted = FALSE,
userFunctionCallEnabled = FALSE) {
.assertIsTrialDesign(design)
.assertIsDatasetMeans(dataInput)
.assertIsValidThetaH0DataInput(thetaH0, dataInput)
.assertIsValidDirectionUpper(directionUpper, design$sided)
.assertIsSingleLogical(normalApproximation, "normalApproximation")
.assertIsValidVarianceOptionMultiArmed(design, varianceOption)
.warnInCaseOfUnknownArguments(
functionName = ".getStageResultsMeansMultiArm",
ignore = c(.getDesignArgumentsToIgnoreAtUnknownArgumentCheck(design, powerCalculationEnabled = TRUE), "stage"), ...
)
stage <- .getStageFromOptionalArguments(..., dataInput = dataInput, design = design)
gMax <- dataInput$getNumberOfGroups() - 1
kMax <- design$kMax
if (.isTrialDesignConditionalDunnett(design)) {
if (!normalApproximation) {
if (userFunctionCallEnabled) {
warning("'normalApproximation' was set to TRUE ",
"because conditional Dunnett test was specified as design",
call. = FALSE
)
}
normalApproximation <- TRUE
}
}
intersectionTest <- .getCorrectedIntersectionTestMultiArmIfNecessary(
design, intersectionTest, userFunctionCallEnabled
)
.assertIsValidIntersectionTestMultiArm(design, intersectionTest)
if (intersectionTest == "Dunnett" && varianceOption != "overallPooled" &&
!normalApproximation) {
stop("Dunnett t test can only be performed with overall variance estimation,
select 'varianceOption' = \"overallPooled\"", call. = FALSE)
}
stageResults <- StageResultsMultiArmMeans(
design = design,
dataInput = dataInput,
thetaH0 = thetaH0,
direction = ifelse(directionUpper, C_DIRECTION_UPPER, C_DIRECTION_LOWER),
normalApproximation = normalApproximation,
directionUpper = directionUpper,
varianceOption = varianceOption,
stage = stage
)
.setValueAndParameterType(
stageResults, "intersectionTest",
intersectionTest, C_INTERSECTION_TEST_MULTIARMED_DEFAULT
)
effectSizes <- matrix(NA_real_, nrow = gMax, ncol = kMax)
overallStDevs <- matrix(NA_real_, nrow = gMax, ncol = kMax)
overallPooledStDevs <- matrix(rep(NA_real_, kMax), 1, kMax)
testStatistics <- matrix(NA_real_, nrow = gMax, ncol = kMax)
overallTestStatistics <- matrix(NA_real_, nrow = gMax, ncol = kMax)
separatePValues <- matrix(NA_real_, nrow = gMax, ncol = kMax)
overallPValues <- matrix(NA_real_, nrow = gMax, ncol = kMax)
dimnames(testStatistics) <- list(paste("arm ", 1:gMax, sep = ""), paste("stage ", (1:kMax), sep = ""))
dimnames(overallTestStatistics) <- list(
paste("arm ", 1:gMax, sep = ""),
paste("stage ", (1:kMax), sep = "")
)
dimnames(separatePValues) <- list(paste("arm ", 1:gMax, sep = ""), paste("stage ", (1:kMax), sep = ""))
dimnames(overallPValues) <- list(paste("arm ", 1:gMax, sep = ""), paste("stage ", (1:kMax), sep = ""))
for (k in 1:stage) {
overallPooledStDevs[1, k] <- sqrt(sum((dataInput$getOverallSampleSizes(stage = k) - 1) *
dataInput$getOverallStDevs(stage = k)^2, na.rm = TRUE) /
sum(dataInput$getOverallSampleSizes(stage = k) - 1, na.rm = TRUE))
if (varianceOption == "overallPooled") {
stDev <- sqrt(sum((dataInput$getSampleSizes(stage = k) - 1) *
dataInput$getStDevs(stage = k)^2, na.rm = TRUE) /
sum(dataInput$getSampleSizes(stage = k) - 1, na.rm = TRUE))
overallStDevForTest <- overallPooledStDevs[1, k]
}
for (treatmentArm in 1:gMax) {
effectSizes[treatmentArm, k] <- dataInput$getOverallMeans(stage = k, group = treatmentArm) -
dataInput$getOverallMeans(stage = k, group = gMax + 1)
overallStDevs[treatmentArm, k] <- sqrt(sum((
dataInput$getOverallSampleSize(stage = k, group = c(treatmentArm, gMax + 1)) - 1) *
dataInput$getOverallStDev(stage = k, group = c(treatmentArm, gMax + 1))^2, na.rm = TRUE) /
sum(dataInput$getOverallSampleSize(stage = k, group = c(treatmentArm, gMax + 1)) - 1))
if (varianceOption == "pairwisePooled") {
stDev <- sqrt(sum((dataInput$getSampleSizes(stage = k, group = c(treatmentArm, gMax + 1)) - 1) *
dataInput$getStDevs(stage = k, group = c(treatmentArm, gMax + 1))^2, na.rm = TRUE) /
sum(dataInput$getSampleSizes(stage = k, group = c(treatmentArm, gMax + 1)) - 1))
overallStDevForTest <- overallStDevs[treatmentArm, k]
}
if (varianceOption == "notPooled") {
testStatistics[treatmentArm, k] <- (dataInput$getMeans(stage = k, group = treatmentArm) -
dataInput$getMeans(stage = k, group = gMax + 1) - thetaH0) /
sqrt(dataInput$getStDevs(stage = k, group = treatmentArm)^2 /
dataInput$getSampleSizes(stage = k, group = treatmentArm) +
dataInput$getStDevs(stage = k, group = gMax + 1)^2 /
dataInput$getSampleSizes(stage = k, group = gMax + 1))
overallTestStatistics[treatmentArm, k] <- (
dataInput$getOverallMeans(stage = k, group = treatmentArm) -
dataInput$getOverallMeans(stage = k, group = gMax + 1) - thetaH0) /
sqrt(dataInput$getOverallStDevs(stage = k, group = treatmentArm)^2 /
dataInput$getOverallSampleSizes(stage = k, group = treatmentArm) +
dataInput$getOverallStDevs(stage = k, group = gMax + 1)^2 /
dataInput$getOverallSampleSizes(stage = k, group = gMax + 1))
} else {
testStatistics[treatmentArm, k] <- (dataInput$getMeans(stage = k, group = treatmentArm) -
dataInput$getMeans(stage = k, group = gMax + 1) - thetaH0) / stDev /
sqrt(1 / dataInput$getSampleSizes(stage = k, group = treatmentArm) + 1 /
dataInput$getSampleSizes(stage = k, group = gMax + 1))
overallTestStatistics[treatmentArm, k] <- (
dataInput$getOverallMeans(stage = k, group = treatmentArm) -
dataInput$getOverallMeans(stage = k, group = gMax + 1) - thetaH0) /
overallStDevForTest /
sqrt(1 / dataInput$getOverallSampleSizes(stage = k, group = treatmentArm) + 1 /
dataInput$getOverallSampleSizes(stage = k, group = gMax + 1))
}
if (normalApproximation) {
separatePValues[treatmentArm, k] <- 1 - stats::pnorm(testStatistics[treatmentArm, k])
overallPValues[treatmentArm, k] <- 1 - stats::pnorm(overallTestStatistics[treatmentArm, k])
} else {
if (varianceOption == "overallPooled") {
separatePValues[treatmentArm, k] <- 1 - stats::pt(
testStatistics[treatmentArm, k],
sum(dataInput$getSampleSizes(stage = k) - 1, na.rm = TRUE)
)
overallPValues[treatmentArm, k] <- 1 - stats::pt(
overallTestStatistics[treatmentArm, k],
sum(dataInput$getOverallSampleSizes(stage = k) - 1, na.rm = TRUE)
)
} else if (varianceOption == "pairwisePooled") {
separatePValues[treatmentArm, k] <- 1 - stats::pt(
testStatistics[treatmentArm, k],
sum(dataInput$getSampleSizes(stage = k, group = c(treatmentArm, gMax + 1)) - 1)
)
overallPValues[treatmentArm, k] <- 1 - stats::pt(
overallTestStatistics[treatmentArm, k],
sum(dataInput$getOverallSampleSizes(stage = k, group = c(treatmentArm, gMax + 1)) - 1)
)
} else if (varianceOption == "notPooled") {
u <- dataInput$getStDevs(stage = k, group = treatmentArm)^2 /
dataInput$getSampleSizes(stage = k, group = treatmentArm) /
(dataInput$getStDevs(stage = k, group = treatmentArm)^2 /
dataInput$getSampleSizes(stage = k, group = treatmentArm) +
dataInput$getStDevs(stage = k, group = gMax + 1)^2 /
dataInput$getSampleSizes(stage = k, group = gMax + 1))
separatePValues[treatmentArm, k] <- 1 - stats::pt(
testStatistics[treatmentArm, k],
1 / (u^2 / (dataInput$getSampleSizes(stage = k, group = treatmentArm) - 1) +
(1 - u)^2 / (dataInput$getSampleSizes(stage = k, group = gMax + 1) - 1))
)
u <- dataInput$getOverallStDevs(stage = k, group = treatmentArm)^2 /
dataInput$getOverallSampleSizes(stage = k, group = treatmentArm) /
(dataInput$getOverallStDevs(stage = k, group = treatmentArm)^2 /
dataInput$getOverallSampleSizes(stage = k, group = treatmentArm) +
dataInput$getOverallStDevs(stage = k, group = gMax + 1)^2 /
dataInput$getOverallSampleSizes(stage = k, group = gMax + 1))
overallPValues[treatmentArm, k] <- 1 - stats::pt(
overallTestStatistics[treatmentArm, k],
1 / (u^2 / (dataInput$getOverallSampleSizes(stage = k, group = treatmentArm) - 1) +
(1 - u)^2 / (dataInput$getOverallSampleSizes(stage = k, group = gMax + 1) - 1))
)
}
}
if (!directionUpper) {
separatePValues[treatmentArm, k] <- 1 - separatePValues[treatmentArm, k]
overallPValues[treatmentArm, k] <- 1 - overallPValues[treatmentArm, k]
# testStatistics[treatmentArm, k] <- -testStatistics[treatmentArm, k]
# overallTestStatistics[treatmentArm, k] <- -overallTestStatistics[treatmentArm, k]
}
}
}
.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]))
sampleSizesSelected <- as.numeric(na.omit(
dataInput$getSampleSizes(stage = k, group = -(gMax + 1))
))
sigma <- sqrt(sampleSizesSelected /
(sampleSizesSelected + dataInput$getSampleSize(k, gMax + 1))) %*%
sqrt(t(sampleSizesSelected / (sampleSizesSelected +
dataInput$getSampleSize(k, gMax + 1))))
diag(sigma) <- 1
for (treatmentArm in 1:gMax) {
if (intersectionTest == "Bonferroni" || intersectionTest == "Simes") {
if (.isTrialDesignGroupSequential(design)) {
overallPValues[treatmentArm, k] <- min(1, overallPValues[treatmentArm, k] * selected)
} else {
singleStepAdjustedPValues[treatmentArm, k] <- min(
1,
separatePValues[treatmentArm, k] * selected
)
}
} else if (intersectionTest == "Sidak") {
if (.isTrialDesignGroupSequential(design)) {
overallPValues[treatmentArm, k] <- 1 - (1 - overallPValues[treatmentArm, k])^selected
} else {
singleStepAdjustedPValues[treatmentArm, k] <- 1 - (1 -
separatePValues[treatmentArm, k])^selected
}
} else if (intersectionTest == "Dunnett") {
if (!is.na(testStatistics[treatmentArm, k])) {
df <- NA_real_
if (!normalApproximation) {
df <- sum(dataInput$getSampleSizes(stage = k) - 1, na.rm = TRUE)
}
singleStepAdjustedPValues[treatmentArm, k] <- 1 - .getMultivariateDistribution(
type = ifelse(normalApproximation, "normal", "t"),
upper = ifelse(directionUpper,
testStatistics[treatmentArm, k], -testStatistics[treatmentArm, k]
),
sigma = sigma, df = df
)
}
}
if (.isTrialDesignInverseNormal(design)) {
combInverseNormal[treatmentArm, k] <- (weightsInverseNormal[1:k] %*%
.getOneMinusQNorm(singleStepAdjustedPValues[treatmentArm, 1:k])) /
sqrt(sum(weightsInverseNormal[1:k]^2))
} else if (.isTrialDesignFisher(design)) {
combFisher[treatmentArm, k] <- prod(
singleStepAdjustedPValues[treatmentArm, 1:k]^weightsFisher[1:k]
)
}
}
}
stageResults$overallTestStatistics <- overallTestStatistics
stageResults$overallPValues <- overallPValues
stageResults$effectSizes <- effectSizes
stageResults$overallStDevs <- overallStDevs
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$overallTestStatistics <- overallTestStatistics
stageResults$overallPValues <- overallPValues
stageResults$effectSizes <- effectSizes
stageResults$overallStDevs <- overallStDevs
stageResults$overallPooledStDevs <- overallPooledStDevs
stageResults$testStatistics <- testStatistics
stageResults$separatePValues <- separatePValues
}
return(stageResults)
}
.getRootThetaMeansMultiArm <- function(..., design, dataInput, treatmentArm, stage,
directionUpper, normalApproximation, varianceOption, intersectionTest,
thetaLow, thetaUp, firstParameterName, secondValue, tolerance) {
result <- .getOneDimensionalRoot(
function(theta) {
stageResults <- .getStageResultsMeansMultiArm(
design = design, dataInput = dataInput,
stage = stage, thetaH0 = theta, directionUpper = directionUpper,
intersectionTest = intersectionTest, normalApproximation = normalApproximation,
varianceOption = varianceOption, calculateSingleStepAdjusted = TRUE
)
firstValue <- stageResults[[firstParameterName]][treatmentArm, stage]
if (.isTrialDesignGroupSequential(design)) {
firstValue <- .getOneMinusQNorm(firstValue)
}
return(firstValue - secondValue)
},
lower = thetaLow, upper = thetaUp, tolerance = tolerance,
callingFunctionInformation = ".getRootThetaMeansMultiArm"
)
return(result)
}
.getUpperLowerThetaMeansMultiArm <- function(..., design, dataInput, theta, treatmentArm, stage,
directionUpper, normalApproximation, varianceOption, conditionFunction, intersectionTest,
firstParameterName, secondValue) {
stageResults <- .getStageResultsMeansMultiArm(
design = design, dataInput = dataInput,
stage = stage, thetaH0 = theta, directionUpper = directionUpper,
intersectionTest = intersectionTest, normalApproximation = normalApproximation,
varianceOption = varianceOption, calculateSingleStepAdjusted = TRUE
)
firstValue <- stageResults[[firstParameterName]][treatmentArm, stage]
maxSearchIterations <- 30
while (conditionFunction(secondValue, firstValue)) {
theta <- 2 * theta
stageResults <- .getStageResultsMeansMultiArm(
design = design, dataInput = dataInput,
stage = stage, thetaH0 = theta, directionUpper = directionUpper,
intersectionTest = intersectionTest, normalApproximation = normalApproximation,
varianceOption = varianceOption, 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)
}
.getRepeatedConfidenceIntervalsMeansMultiArmAll <- function(...,
design, dataInput,
directionUpper = C_DIRECTION_UPPER_DEFAULT,
normalApproximation = C_NORMAL_APPROXIMATION_MEANS_DEFAULT,
varianceOption = C_VARIANCE_OPTION_MULTIARMED_DEFAULT,
intersectionTest = C_INTERSECTION_TEST_MULTIARMED_DEFAULT,
tolerance = C_ANALYSIS_TOLERANCE_DEFAULT,
firstParameterName) {
.assertIsValidIntersectionTestMultiArm(design, intersectionTest)
stage <- .getStageFromOptionalArguments(..., dataInput = dataInput, design = design)
stageResults <- .getStageResultsMeansMultiArm(
design = design, dataInput = dataInput,
stage = stage, thetaH0 = 0, directionUpper = directionUpper,
intersectionTest = intersectionTest, normalApproximation = normalApproximation,
varianceOption = varianceOption, calculateSingleStepAdjusted = FALSE
)
gMax <- dataInput$getNumberOfGroups() - 1
repeatedConfidenceIntervals <- array(NA_real_, dim = c(gMax, 2, design$kMax))
# Confidence interval for second stage when using conditional Dunnett test
if (.isTrialDesignConditionalDunnett(design)) {
startTime <- Sys.time()
for (treatmentArm in 1:gMax) {
if (!is.na(stageResults$testStatistics[treatmentArm, 2])) {
thetaLowLimit <- -1
iteration <- 30
rejected <- FALSE
while (!rejected && iteration >= 0) {
stageResults <- .getStageResultsMeansMultiArm(
design = design, dataInput = dataInput,
stage = stage, thetaH0 = thetaLowLimit, directionUpper = TRUE,
intersectionTest = intersectionTest, normalApproximation = normalApproximation,
varianceOption = varianceOption, calculateSingleStepAdjusted = FALSE
)
rejected <- .getConditionalDunnettTestForCI(
design = design,
stageResults = stageResults, treatmentArm = treatmentArm
)
iteration <- iteration - 1
thetaLowLimit <- 2 * thetaLowLimit
}
iteration <- 30
thetaUpLimit <- 1
rejected <- FALSE
while (!rejected && iteration >= 0) {
stageResults <- .getStageResultsMeansMultiArm(
design = design, dataInput = dataInput,
stage = stage, thetaH0 = thetaUpLimit, directionUpper = FALSE,
intersectionTest = intersectionTest, normalApproximation = normalApproximation,
varianceOption = varianceOption, calculateSingleStepAdjusted = FALSE
)
rejected <- .getConditionalDunnettTestForCI(
design = design,
stageResults = stageResults, treatmentArm = treatmentArm
)
iteration <- iteration - 1
thetaUpLimit <- 2 * thetaUpLimit
}
thetaLow <- thetaLowLimit
thetaUp <- thetaUpLimit
iteration <- 30
prec <- 1
while (prec > tolerance) {
theta <- (thetaLow + thetaUp) / 2
stageResults <- .getStageResultsMeansMultiArm(
design = design, dataInput = dataInput,
stage = stage, thetaH0 = theta, directionUpper = TRUE,
intersectionTest = intersectionTest, normalApproximation = normalApproximation,
varianceOption = varianceOption, calculateSingleStepAdjusted = FALSE
)
conditionalDunnettSingleStepRejected <- .getConditionalDunnettTestForCI(
design = design, stageResults = stageResults, treatmentArm = treatmentArm
)
ifelse(conditionalDunnettSingleStepRejected, thetaLow <- theta, thetaUp <- theta)
ifelse(iteration > 0, prec <- thetaUp - thetaLow, prec <- 0)
iteration <- iteration - 1
}
repeatedConfidenceIntervals[treatmentArm, 1, 2] <- theta
thetaLow <- thetaLowLimit
thetaUp <- thetaUpLimit
iteration <- 30
prec <- 1
while (prec > tolerance) {
theta <- (thetaLow + thetaUp) / 2
stageResults <- .getStageResultsMeansMultiArm(
design = design, dataInput = dataInput,
stage = stage, thetaH0 = theta, directionUpper = FALSE,
intersectionTest = intersectionTest, normalApproximation = normalApproximation,
varianceOption = varianceOption, calculateSingleStepAdjusted = FALSE
)
conditionalDunnettSingleStepRejected <- .getConditionalDunnettTestForCI(
design = design, stageResults = stageResults, treatmentArm = treatmentArm
)
ifelse(conditionalDunnettSingleStepRejected, thetaUp <- theta, thetaLow <- theta)
ifelse(iteration > 0, prec <- thetaUp - thetaLow, prec <- 0)
iteration <- iteration - 1
}
repeatedConfidenceIntervals[treatmentArm, 2, 2] <- theta
if (!is.na(repeatedConfidenceIntervals[treatmentArm, 1, 2]) &&
!is.na(repeatedConfidenceIntervals[treatmentArm, 2, 2]) &&
repeatedConfidenceIntervals[treatmentArm, 1, 2] >
repeatedConfidenceIntervals[treatmentArm, 2, 2]) {
repeatedConfidenceIntervals[treatmentArm, , 2] <- rep(NA_real_, 2)
}
}
}
.logProgress("Confidence intervals for final stage calculated", startTime = startTime)
} else {
# Repeated onfidence intervals when using combination tests
if (intersectionTest == "Hierarchical") {
warning("Repeated confidence intervals not available for ",
"'intersectionTest' = \"Hierarchical\"",
call. = FALSE
)
return(repeatedConfidenceIntervals)
}
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
}
# Necessary for adjustment for binding futility boundaries
futilityCorr <- rep(NA_real_, design$kMax)
stages <- (1:stage)
for (k in stages) {
startTime <- Sys.time()
for (treatmentArm in 1:gMax) {
if (!is.na(stageResults$testStatistics[treatmentArm, k]) && criticalValues[k] < C_QNORM_MAXIMUM) {
# finding maximum upper and minimum lower bounds for RCIs
thetaLow <- .getUpperLowerThetaMeansMultiArm(
design = design, dataInput = dataInput,
theta = -1, treatmentArm = treatmentArm, stage = k, directionUpper = TRUE,
normalApproximation = normalApproximation, varianceOption = varianceOption,
conditionFunction = conditionFunction,
intersectionTest = intersectionTest, firstParameterName = firstParameterName,
secondValue = criticalValues[k]
)
thetaUp <- .getUpperLowerThetaMeansMultiArm(
design = design, dataInput = dataInput,
theta = 1, treatmentArm = treatmentArm, stage = k, directionUpper = FALSE,
normalApproximation = normalApproximation, varianceOption = varianceOption,
conditionFunction = conditionFunction,
intersectionTest = intersectionTest, firstParameterName = firstParameterName,
secondValue = criticalValues[k]
)
# finding upper and lower RCI limits through root function
repeatedConfidenceIntervals[treatmentArm, 1, k] <- .getRootThetaMeansMultiArm(
design = design,
dataInput = dataInput, treatmentArm = treatmentArm, stage = k, directionUpper = TRUE,
normalApproximation = normalApproximation, varianceOption = varianceOption,
thetaLow = thetaLow, thetaUp = thetaUp,
intersectionTest = intersectionTest, firstParameterName = firstParameterName,
secondValue = criticalValues[k], tolerance = tolerance
)
repeatedConfidenceIntervals[treatmentArm, 2, k] <- .getRootThetaMeansMultiArm(
design = design,
dataInput = dataInput, treatmentArm = treatmentArm, stage = k, directionUpper = FALSE,
normalApproximation = normalApproximation, varianceOption = varianceOption,
thetaLow = thetaLow, thetaUp = thetaUp,
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 <- .getUpperLowerThetaMeansMultiArm(
design = design,
dataInput = dataInput,
theta = -1, treatmentArm = treatmentArm, stage = k - 1, directionUpper = TRUE,
normalApproximation = normalApproximation, varianceOption = varianceOption,
conditionFunction = conditionFunction,
intersectionTest = intersectionTest, firstParameterName = parameterName,
secondValue = bounds[k - 1]
)
} else {
thetaUp <- .getUpperLowerThetaMeansMultiArm(
design = design,
dataInput = dataInput,
theta = 1, treatmentArm = treatmentArm, stage = k - 1, directionUpper = FALSE,
normalApproximation = normalApproximation, varianceOption = varianceOption,
conditionFunction = conditionFunction,
intersectionTest = intersectionTest, firstParameterName = parameterName,
secondValue = bounds[k - 1]
)
}
futilityCorr[k] <- .getRootThetaMeansMultiArm(
design = design, dataInput = dataInput,
treatmentArm = treatmentArm, stage = k - 1, directionUpper = directionUpper,
normalApproximation = normalApproximation, varianceOption = varianceOption,
thetaLow = thetaLow, thetaUp = thetaUp,
intersectionTest = intersectionTest, firstParameterName = parameterName,
secondValue = bounds[k - 1], tolerance = tolerance
)
if (directionUpper) {
repeatedConfidenceIntervals[treatmentArm, 1, k] <- min(
min(futilityCorr[2:k]),
repeatedConfidenceIntervals[treatmentArm, 1, k]
)
} else {
repeatedConfidenceIntervals[treatmentArm, 2, k] <- max(
max(futilityCorr[2:k]),
repeatedConfidenceIntervals[treatmentArm, 2, k]
)
}
}
if (!is.na(repeatedConfidenceIntervals[treatmentArm, 1, k]) &&
!is.na(repeatedConfidenceIntervals[treatmentArm, 2, k]) &&
repeatedConfidenceIntervals[treatmentArm, 1, k] >
repeatedConfidenceIntervals[treatmentArm, 2, k]) {
repeatedConfidenceIntervals[treatmentArm, , 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
#'
.getRepeatedConfidenceIntervalsMeansMultiArmInverseNormal <- function(...,
design, dataInput,
normalApproximation = C_NORMAL_APPROXIMATION_MEANS_DEFAULT,
varianceOption = C_VARIANCE_OPTION_MULTIARMED_DEFAULT,
directionUpper = C_DIRECTION_UPPER_DEFAULT,
intersectionTest = C_INTERSECTION_TEST_MULTIARMED_DEFAULT,
tolerance = C_ANALYSIS_TOLERANCE_DEFAULT) {
.warnInCaseOfUnknownArguments(
functionName =
".getRepeatedConfidenceIntervalsMeansMultiArmInverseNormal",
ignore = c(.getDesignArgumentsToIgnoreAtUnknownArgumentCheck(design, powerCalculationEnabled = TRUE), "stage"), ...
)
return(.getRepeatedConfidenceIntervalsMeansMultiArmAll(
design = design, dataInput = dataInput,
normalApproximation = normalApproximation, varianceOption = varianceOption,
directionUpper = directionUpper, intersectionTest = intersectionTest,
tolerance = tolerance, firstParameterName = "combInverseNormal", ...
))
}
#'
#' RCIs based on Fisher's combination test
#'
#' @noRd
#'
.getRepeatedConfidenceIntervalsMeansMultiArmFisher <- function(...,
design, dataInput,
normalApproximation = C_NORMAL_APPROXIMATION_MEANS_DEFAULT,
varianceOption = C_VARIANCE_OPTION_MULTIARMED_DEFAULT,
directionUpper = C_DIRECTION_UPPER_DEFAULT,
intersectionTest = C_INTERSECTION_TEST_MULTIARMED_DEFAULT,
tolerance = C_ANALYSIS_TOLERANCE_DEFAULT) {
.warnInCaseOfUnknownArguments(
functionName =
".getRepeatedConfidenceIntervalsMeansMultiArmFisher",
ignore = c(.getDesignArgumentsToIgnoreAtUnknownArgumentCheck(design, powerCalculationEnabled = TRUE), "stage"), ...
)
return(.getRepeatedConfidenceIntervalsMeansMultiArmAll(
design = design, dataInput = dataInput,
normalApproximation = normalApproximation, varianceOption = varianceOption,
directionUpper = directionUpper, intersectionTest = intersectionTest,
tolerance = tolerance, firstParameterName = "combFisher", ...
))
}
#'
#' CIs based on conditional Dunnett test
#'
#' @noRd
#'
.getRepeatedConfidenceIntervalsMeansMultiArmConditionalDunnett <- function(...,
design, dataInput,
normalApproximation = C_NORMAL_APPROXIMATION_MEANS_DEFAULT,
varianceOption = C_VARIANCE_OPTION_MULTIARMED_DEFAULT,
directionUpper = C_DIRECTION_UPPER_DEFAULT,
intersectionTest = C_INTERSECTION_TEST_MULTIARMED_DEFAULT,
tolerance = C_ANALYSIS_TOLERANCE_DEFAULT) {
.warnInCaseOfUnknownArguments(
functionName =
".getRepeatedConfidenceIntervalsMeansMultiArmConditionalDunnett",
ignore = c(.getDesignArgumentsToIgnoreAtUnknownArgumentCheck(
design,
powerCalculationEnabled = TRUE
), "stage"), ...
)
return(.getRepeatedConfidenceIntervalsMeansMultiArmAll(
design = design, dataInput = dataInput,
normalApproximation = normalApproximation, varianceOption = varianceOption,
directionUpper = directionUpper, intersectionTest = intersectionTest,
tolerance = tolerance, firstParameterName = NA, ...
))
}
#'
#' Calculation of lower and upper limits of repeated confidence intervals (RCIs) for Means
#'
#' @noRd
#'
.getRepeatedConfidenceIntervalsMeansMultiArm <- function(..., design) {
if (.isTrialDesignInverseNormal(design)) {
return(.getRepeatedConfidenceIntervalsMeansMultiArmInverseNormal(design = design, ...))
}
if (.isTrialDesignFisher(design)) {
return(.getRepeatedConfidenceIntervalsMeansMultiArmFisher(design = design, ...))
}
if (.isTrialDesignConditionalDunnett(design)) {
return(.getRepeatedConfidenceIntervalsMeansMultiArmConditionalDunnett(design = design, ...))
}
.stopWithWrongDesignMessage(design)
}
#'
#' Calculation of conditional power for Means
#'
#' @noRd
#'
.getConditionalPowerMeansMultiArm <- function(..., stageResults, stage = stageResults$stage,
nPlanned, allocationRatioPlanned = C_ALLOCATION_RATIO_DEFAULT,
thetaH1 = NA_real_, assumedStDevs = NA_real_,
iterations = C_ITERATIONS_DEFAULT, seed = NA_real_) {
stDevsH1 <- .getOptionalArgument("stDevsH1", ...)
if (!is.null(stDevsH1) && !is.na(stDevsH1)) {
if (!is.na(assumedStDevs)) {
warning(sQuote("assumedStDevs"), " will be ignored because ",
sQuote("stDevsH1"), " is defined",
call. = FALSE
)
}
assumedStDevs <- stDevsH1
}
design <- stageResults$.design
gMax <- stageResults$getGMax()
kMax <- design$kMax
results <- ConditionalPowerResultsMultiArmMeans(
.design = design,
.stageResults = stageResults,
thetaH1 = thetaH1,
assumedStDevs = assumedStDevs,
nPlanned = nPlanned,
allocationRatioPlanned = allocationRatioPlanned
)
if (any(is.na(nPlanned))) {
return(results)
}
.assertIsValidStage(stage, kMax)
if (stage == kMax) {
.logDebug(
"Conditional power will be calculated only for subsequent stages ",
"(stage = ", stage, ", kMax = ", kMax, ")"
)
return(results)
}
if (!.isValidNPlanned(nPlanned = nPlanned, kMax = kMax, stage = stage)) {
return(results)
}
.assertIsValidNPlanned(nPlanned, kMax, stage)
.assertIsSingleNumber(allocationRatioPlanned, "allocationRatioPlanned")
.assertIsInOpenInterval(allocationRatioPlanned, "allocationRatioPlanned", 0, C_ALLOCATION_RATIO_MAXIMUM)
.setValueAndParameterType(results, "allocationRatioPlanned", allocationRatioPlanned, C_ALLOCATION_RATIO_DEFAULT)
assumedStDevs <- .assertIsValidAssumedStDevForMultiHypotheses(
assumedStDevs, stageResults, stage,
results = results
)
.assertIsValidAssumedStDevs(assumedStDevs, gMax)
thetaH1 <- .assertIsValidThetaH1ForMultiArm(thetaH1, stageResults, stage, results = results)
results$.setParameterType("nPlanned", C_PARAM_USER_DEFINED)
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 (length(assumedStDevs) == 1) {
results$assumedStDevs <- rep(assumedStDevs, gMax)
results$.setParameterType("assumedStDevs", C_PARAM_GENERATED)
}
if (.isTrialDesignInverseNormal(design)) {
return(.getConditionalPowerMeansMultiArmInverseNormal(
results = results, stageResults = stageResults, stage = stage,
nPlanned = nPlanned,
allocationRatioPlanned = allocationRatioPlanned,
thetaH1 = thetaH1,
assumedStDevs = assumedStDevs, ...
))
} else if (.isTrialDesignFisher(design)) {
return(.getConditionalPowerMeansMultiArmFisher(
results = results, stageResults = stageResults, stage = stage,
nPlanned = nPlanned,
allocationRatioPlanned = allocationRatioPlanned,
thetaH1 = thetaH1,
assumedStDevs = assumedStDevs,
iterations = iterations, seed = seed, ...
))
} else if (.isTrialDesignConditionalDunnett(design)) {
return(.getConditionalPowerMeansMultiArmConditionalDunnett(
results = results, stageResults = stageResults, stage = stage,
nPlanned = nPlanned,
allocationRatioPlanned = allocationRatioPlanned,
thetaH1 = thetaH1,
assumedStDevs = assumedStDevs, ...
))
}
stop(
C_EXCEPTION_TYPE_ILLEGAL_ARGUMENT,
"'design' must be an instance of TrialDesignInverseNormal, TrialDesignFisher, or ",
"TrialDesignConditionalDunnett"
)
}
#'
#' Calculation of conditional power based on inverse normal method
#'
#' @noRd
#'
.getConditionalPowerMeansMultiArmInverseNormal <- function(..., results, stageResults, stage,
allocationRatioPlanned, nPlanned, thetaH1, assumedStDevs) {
design <- stageResults$.design
.assertIsTrialDesignInverseNormal(design)
.warnInCaseOfUnknownArguments(
functionName = ".getConditionalPowerMeansMultiArmInverseNormal",
ignore = c("stage", "design", "stDevsH1"), ...
)
kMax <- design$kMax
gMax <- stageResults$getGMax()
# results$conditionalPower <- matrix(NA_real_, nrow = gMax, ncol = kMax)
weights <- .getWeightsInverseNormal(design)
informationRates <- design$informationRates
nPlanned <- c(rep(NA_real_, stage), nPlanned)
nPlanned <- allocationRatioPlanned / (1 + allocationRatioPlanned)^2 * nPlanned
if (length(thetaH1) == 1) {
thetaH1 <- rep(thetaH1, gMax)
results$.setParameterType("thetaH1", C_PARAM_GENERATED)
} else {
results$.setParameterType("thetaH1", C_PARAM_DEFAULT_VALUE)
}
results$.setParameterType("assumedStDevs", C_PARAM_DEFAULT_VALUE)
if (stageResults$directionUpper) {
standardizedEffect <- (thetaH1 - stageResults$thetaH0) / assumedStDevs
} else {
standardizedEffect <- -(thetaH1 - stageResults$thetaH0) / assumedStDevs
}
ctr <- .performClosedCombinationTest(stageResults = stageResults)
criticalValues <- design$criticalValues
for (treatmentArm in 1:gMax) {
if (!is.na(ctr$separatePValues[treatmentArm, stage])) {
# shifted decision region for use in getGroupSeqProbs
# Inverse Normal Method
shiftedDecisionRegionUpper <- criticalValues[(stage + 1):kMax] *
sqrt(sum(weights[1:stage]^2) + cumsum(weights[(stage + 1):kMax]^2)) /
sqrt(cumsum(weights[(stage + 1):kMax]^2)) -
min(ctr$overallAdjustedTestStatistics[ctr$indices[, treatmentArm] == 1, stage], na.rm = TRUE) *
sqrt(sum(weights[1:stage]^2)) /
sqrt(cumsum(weights[(stage + 1):kMax]^2)) - standardizedEffect[treatmentArm] *
cumsum(sqrt(nPlanned[(stage + 1):kMax]) * weights[(stage + 1):kMax]) /
sqrt(cumsum(weights[(stage + 1):kMax]^2))
if (stage == kMax - 1) {
shiftedFutilityBounds <- c()
} else {
shiftedFutilityBounds <- design$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[, treatmentArm] == 1, stage], na.rm = TRUE) *
sqrt(sum(weights[1:stage]^2)) /
sqrt(cumsum(weights[(stage + 1):(kMax - 1)]^2)) - standardizedEffect[treatmentArm] *
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$conditionalPower[treatmentArm, (stage + 1):kMax] <- cumsum(probs[3, ] - probs[2, ])
}
}
nPlanned <- (1 + allocationRatioPlanned)^2 / allocationRatioPlanned * nPlanned
results$nPlanned <- nPlanned
results$.setParameterType("nPlanned", C_PARAM_GENERATED)
results$.setParameterType("conditionalPower", C_PARAM_GENERATED)
results$thetaH1 <- thetaH1
results$assumedStDevs <- assumedStDevs
return(results)
}
#'
#' Calculation of conditional power based on Fisher's combination test
#'
#' @noRd
#'
.getConditionalPowerMeansMultiArmFisher <- function(..., results, stageResults, stage,
allocationRatioPlanned, nPlanned, thetaH1, assumedStDevs,
iterations, seed) {
design <- stageResults$.design
.assertIsTrialDesignFisher(design)
.assertIsValidIterationsAndSeed(iterations, seed, zeroIterationsAllowed = FALSE)
.warnInCaseOfUnknownArguments(
functionName = ".getConditionalPowerMeansMultiArmFisher",
ignore = c("stage", "design", "stDevsH1"), ...
)
kMax <- design$kMax
gMax <- stageResults$getGMax()
criticalValues <- design$criticalValues
weightsFisher <- .getWeightsFisher(design)
# results$conditionalPower <- matrix(NA_real_, nrow = gMax, ncol = kMax)
results$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$.setParameterType("simulated", C_PARAM_DEFAULT_VALUE)
if (length(thetaH1) == 1) {
thetaH1 <- rep(thetaH1, gMax)
results$.setParameterType("thetaH1", C_PARAM_GENERATED)
} else {
results$.setParameterType("thetaH1", C_PARAM_DEFAULT_VALUE)
}
results$.setParameterType("assumedStDevs", C_PARAM_DEFAULT_VALUE)
if (stageResults$directionUpper) {
standardizedEffect <- (thetaH1 - stageResults$thetaH0) / assumedStDevs
} else {
standardizedEffect <- -(thetaH1 - stageResults$thetaH0) / assumedStDevs
}
nPlanned <- c(rep(NA_real_, stage), nPlanned)
nPlanned <- allocationRatioPlanned / (1 + allocationRatioPlanned)^2 * nPlanned
ctr <- .performClosedCombinationTest(stageResults = stageResults)
for (treatmentArm in 1:gMax) {
if (!is.na(ctr$separatePValues[treatmentArm, stage])) {
if (gMax == 1) {
pValues <- ctr$adjustedStageWisePValues[ctr$indices[, treatmentArm] == 1, ][1:stage]
} else {
pValues <- ctr$adjustedStageWisePValues[ctr$indices[, treatmentArm] == 1, ][which.max(
ctr$overallAdjustedTestStatistics[ctr$indices[, treatmentArm] == 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$alpha0Vec,
criticalValues = criticalValues, weightsFisher = weightsFisher,
pValues = pValues, currentKMax = k, thetaH1 = standardizedEffect[treatmentArm],
stage = stage, nPlanned = nPlanned
)
}
results$conditionalPower[treatmentArm, 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$conditionalPower[treatmentArm, kMax] <- NA_real_
} else {
results$conditionalPower[treatmentArm, kMax] <- 1 - stats::pnorm(.getQNorm(result) -
standardizedEffect[treatmentArm] * 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
results$assumedStDevs <- assumedStDevs
if (!results$simulated) {
results$iterations <- NA_integer_
results$seed <- NA_real_
results$.setParameterType("iterations", C_PARAM_NOT_APPLICABLE)
results$.setParameterType("seed", C_PARAM_NOT_APPLICABLE)
}
return(results)
}
#'
#' Calculation of conditional power based on conditional Dunnett test
#'
#' @noRd
#'
.getConditionalPowerMeansMultiArmConditionalDunnett <- function(..., results, stageResults, stage,
allocationRatioPlanned, nPlanned, thetaH1, assumedStDevs) {
design <- stageResults$.design
.assertIsTrialDesignConditionalDunnett(design)
.warnInCaseOfUnknownArguments(
functionName = ".getConditionalPowerMeansMultiArmConditionalDunnett",
ignore = c("stage", "intersectionTest", "design", "stDevsH1"), ...
)
if (stage > 1) {
warning("Conditional power is only calculated for the first (interim) stage", call. = FALSE)
}
kMax <- 2
gMax <- stageResults$getGMax()
nPlanned <- c(rep(NA_real_, stage), nPlanned)
nPlanned <- allocationRatioPlanned / (1 + allocationRatioPlanned)^2 * nPlanned
if (length(thetaH1) == 1) {
thetaH1 <- rep(thetaH1, gMax)
results$.setParameterType("thetaH1", C_PARAM_GENERATED)
} else {
results$.setParameterType("thetaH1", C_PARAM_DEFAULT_VALUE)
}
results$.setParameterType("assumedStDevs", C_PARAM_DEFAULT_VALUE)
if (stageResults$directionUpper) {
standardizedEffect <- (thetaH1 - stageResults$thetaH0) / assumedStDevs
} else {
standardizedEffect <- -(thetaH1 - stageResults$thetaH0) / assumedStDevs
}
ctr <- .getClosedConditionalDunnettTestResults(stageResults = stageResults, design = design, stage = stage)
for (treatmentArm in 1:gMax) {
if (!is.na(ctr$separatePValues[treatmentArm, stage])) {
results$conditionalPower[treatmentArm, 2] <- 1 -
stats::pnorm(.getOneMinusQNorm(min(ctr$conditionalErrorRate[
ctr$indices[, treatmentArm] == 1,
stage
], na.rm = TRUE)) - standardizedEffect[treatmentArm] * sqrt(nPlanned[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
results$assumedStDevs <- assumedStDevs
return(results)
}
#'
#' Calculation of conditional power and likelihood values for plotting the graph
#'
#' @noRd
#'
.getConditionalPowerLikelihoodMeansMultiArm <- function(..., stageResults, stage,
nPlanned, allocationRatioPlanned = C_ALLOCATION_RATIO_DEFAULT,
thetaRange, assumedStDevs = NA_real_,
iterations = C_ITERATIONS_DEFAULT, seed = NA_real_) {
.associatedArgumentsAreDefined(nPlanned = nPlanned, thetaRange = thetaRange)
.assertIsSingleNumber(allocationRatioPlanned, "allocationRatioPlanned")
.assertIsInOpenInterval(allocationRatioPlanned, "allocationRatioPlanned", 0, C_ALLOCATION_RATIO_MAXIMUM)
design <- stageResults$.design
kMax <- design$kMax
gMax <- stageResults$getGMax()
intersectionTest <- stageResults$intersectionTest
assumedStDevs <- .assertIsValidAssumedStDevForMultiHypotheses(assumedStDevs, stageResults, stage)
if (length(assumedStDevs) == 1) {
assumedStDevs <- rep(assumedStDevs, gMax)
}
thetaRange <- .assertIsValidThetaRange(thetaRange = thetaRange)
treatmentArms <- numeric(gMax * length(thetaRange))
effectValues <- numeric(gMax * length(thetaRange))
condPowerValues <- numeric(gMax * length(thetaRange))
likelihoodValues <- numeric(gMax * length(thetaRange))
stdErr <- stageResults$overallStDevs[, stage] *
sqrt(1 / stageResults$.dataInput$getOverallSampleSizes(stage = stage, group = gMax + 1) +
1 / stageResults$.dataInput$getOverallSampleSizes(stage = stage, group = (1:gMax)))
results <- ConditionalPowerResultsMultiArmMeans(
.design = design,
.stageResults = stageResults,
assumedStDevs = assumedStDevs,
nPlanned = nPlanned,
allocationRatioPlanned = allocationRatioPlanned
)
j <- 1
for (i in seq(along = thetaRange)) {
for (treatmentArm in 1:gMax) {
treatmentArms[j] <- treatmentArm
effectValues[j] <- thetaRange[i]
if (.isTrialDesignInverseNormal(design)) {
condPowerValues[j] <- .getConditionalPowerMeansMultiArmInverseNormal(
results = results,
stageResults = stageResults, stage = stage, nPlanned = nPlanned,
allocationRatioPlanned = allocationRatioPlanned,
thetaH1 = thetaRange[i], assumedStDevs = assumedStDevs
)$conditionalPower[treatmentArm, kMax]
} else if (.isTrialDesignFisher(design)) {
condPowerValues[j] <- .getConditionalPowerMeansMultiArmFisher(
results = results,
stageResults = stageResults, stage = stage, nPlanned = nPlanned,
allocationRatioPlanned = allocationRatioPlanned,
thetaH1 = thetaRange[i], assumedStDevs = assumedStDevs,
iterations = iterations, seed = seed
)$conditionalPower[treatmentArm, kMax]
} else if (.isTrialDesignConditionalDunnett(design)) {
condPowerValues[j] <- .getConditionalPowerMeansMultiArmConditionalDunnett(
results = results,
stageResults = stageResults, stage = stage, nPlanned = nPlanned,
allocationRatioPlanned = allocationRatioPlanned,
thetaH1 = thetaRange[i], assumedStDevs = assumedStDevs
)$conditionalPower[treatmentArm, 2]
}
likelihoodValues[j] <- stats::dnorm(
thetaRange[i],
stageResults$effectSizes[treatmentArm, stage], stdErr[treatmentArm]
) /
stats::dnorm(0, 0, stdErr[treatmentArm])
j <- j + 1
}
}
subtitle <- paste0(
"Intersection test = ", intersectionTest,
", stage = ", stage, ", # of remaining subjects = ",
sum(nPlanned), ", sd = ", .formatSubTitleValue(assumedStDevs, "assumedStDevs"),
", allocation ratio = ", .formatSubTitleValue(allocationRatioPlanned, "allocationRatioPlanned")
)
return(list(
treatmentArms = treatmentArms,
xValues = effectValues,
condPowerValues = condPowerValues,
likelihoodValues = likelihoodValues,
main = C_PLOT_MAIN_CONDITIONAL_POWER_WITH_LIKELIHOOD,
xlab = "Effect size",
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.