Nothing
R/class_design_plan.R
In rpact: Confirmatory Adaptive Clinical Trial Design and Analysis
Defines functions
as.data.frame.TrialDesignPlan
Documented in
as.data.frame.TrialDesignPlan
## |
## | *Trial design plan classes*
## |
## | 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: 7651 $
## | Last changed: $Date: 2024-02-20 15:45:44 +0100 (Di, 20 Feb 2024) $
## | Last changed by: $Author: pahlke $
## |
#' @include f_core_constants.R
#' @include f_design_general_utilities.R
NULL
C_VARIABLE_DESIGN_PLAN_PARAMETERS <- c("lambda1", "pi1", "median1", "alternative", "hazardRatio", "theta")
C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_MEANS <- list(
normalApproximation = FALSE,
meanRatio = FALSE,
thetaH0 = 0,
alternative = seq(0.2, 1, 0.2),
stDev = 1,
groups = 2L,
allocationRatioPlanned = 1
)
C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_RATES <- list(
normalApproximation = TRUE,
riskRatio = FALSE,
thetaH0 = 0,
pi1 = C_PI_1_SAMPLE_SIZE_DEFAULT,
pi2 = C_PI_2_DEFAULT,
groups = 2L,
allocationRatioPlanned = 1
)
C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_SURVIVAL <- list(
typeOfComputation = "Schoenfeld",
thetaH0 = 1,
pi2 = C_PI_2_DEFAULT,
pi1 = C_PI_1_SAMPLE_SIZE_DEFAULT,
allocationRatioPlanned = 1,
accountForObservationTimes = NA,
eventTime = 12,
accrualTime = C_ACCRUAL_TIME_DEFAULT,
accrualIntensity = C_ACCRUAL_INTENSITY_DEFAULT,
kappa = 1,
piecewiseSurvivalTime = NA_real_,
lambda2 = NA_real_,
lambda1 = NA_real_,
followUpTime = C_FOLLOW_UP_TIME_DEFAULT,
maxNumberOfSubjects = 0,
dropoutRate1 = 0,
dropoutRate2 = 0,
dropoutTime = 12
)
C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_COUNT_DATA <- list(
thetaH0 = 1,
overdispersion = 0,
allocationRatioPlanned = 1
)
#'
#' @name TrialDesignPlan
#'
#' @title
#' Basic Trial Design Plan
#'
#' @description
#' Basic class for trial design plans.
#'
#' @details
#' \code{TrialDesignPlan} is the basic class for
#' \itemize{
#' \item \code{\link{TrialDesignPlanMeans}},
#' \item \code{\link{TrialDesignPlanRates}}, and
#' \item \code{\link{TrialDesignPlanSurvival}}.
#' }
#'
#' @include f_core_constants.R
#' @include f_core_utilities.R
#' @include class_core_parameter_set.R
#' @include class_core_plot_settings.R
#' @include class_design.R
#' @include class_design_set.R
#' @include f_core_plot.R
#'
#' @keywords internal
#'
#' @importFrom methods new
#'
TrialDesignPlan <- setRefClass("TrialDesignPlan",
contains = "ParameterSet",
fields = list(
.plotSettings = "PlotSettings",
.design = "TrialDesign",
.objectType = "character" # "sampleSize" or "power"
),
methods = list(
initialize = function(design, ...) {
callSuper(.design = design, ...)
.plotSettings <<- PlotSettings()
if (.isTrialDesignPlanMeans(.self)) {
defaultValueList <- C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_MEANS
} else if (.isTrialDesignPlanRates(.self)) {
defaultValueList <- C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_RATES
} else if (.isTrialDesignPlanSurvival(.self)) {
defaultValueList <- C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_SURVIVAL
} else if (.isTrialDesignPlanCountData(.self)) {
defaultValueList <- C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_COUNT_DATA
}
for (parameterName in .getVisibleFieldNames()) {
defaultValue <- defaultValueList[[parameterName]]
existingValue <- .self[[parameterName]]
if (all(is.na(existingValue))) {
.setParameterType(parameterName, C_PARAM_DEFAULT_VALUE)
} else if (!is.null(defaultValue) && length(defaultValue) == length(existingValue) &&
!any(is.na(defaultValue)) && !any(is.na(existingValue)) &&
sum(defaultValue == existingValue) == length(defaultValue)) {
.setParameterType(parameterName, C_PARAM_DEFAULT_VALUE)
} else {
.setParameterType(parameterName, C_PARAM_USER_DEFINED)
}
}
.setParameterType("optimumAllocationRatio", C_PARAM_NOT_APPLICABLE)
},
.setObjectType = function(objectType) {
if (length(objectType) == 0 || !(objectType %in% c("sampleSize", "power"))) {
stop(
C_EXCEPTION_TYPE_RUNTIME_ISSUE, "'.objectType' (", objectType,
") must be specified as 'sampleSize' or 'power'"
)
}
.objectType <<- objectType
},
.isSampleSizeObject = function() {
if (length(.objectType) == 0 || !(.objectType %in% c("sampleSize", "power"))) {
stop(C_EXCEPTION_TYPE_RUNTIME_ISSUE, "'.objectType' must be specified as 'sampleSize' or 'power'")
}
return(.objectType == "sampleSize")
},
.isPowerObject = function() {
if (length(.objectType) == 0 || !(.objectType %in% c("sampleSize", "power"))) {
stop(C_EXCEPTION_TYPE_RUNTIME_ISSUE, "'.objectType' must be specified as 'sampleSize' or 'power'")
}
return(.objectType == "power")
},
getPlotSettings = function() {
return(.plotSettings)
},
show = function(showType = 1, digits = NA_integer_) {
.show(showType = showType, digits = digits, consoleOutputEnabled = TRUE)
},
.show = function(showType = 1, digits = NA_integer_, consoleOutputEnabled = TRUE) {
"Method for automatically printing trial plan objects"
.resetCat()
if (showType == 3) {
.createSummary(.self, digits = digits)$.show(
showType = 1,
digits = digits, consoleOutputEnabled = consoleOutputEnabled
)
} else if (showType == 2) {
callSuper(showType = showType, digits = digits, consoleOutputEnabled = consoleOutputEnabled)
} else {
.cat("Design plan parameters and output for ", .toString(), ":\n\n",
heading = 1,
consoleOutputEnabled = consoleOutputEnabled
)
.showParametersOfOneGroup(.getDesignParametersToShow(.self), "Design parameters",
orderByParameterName = FALSE, consoleOutputEnabled = consoleOutputEnabled
)
.showParametersOfOneGroup(.getUserDefinedParameters(), "User defined parameters",
orderByParameterName = FALSE, consoleOutputEnabled = consoleOutputEnabled
)
.showParametersOfOneGroup(.getDefaultParameters(), "Default parameters",
orderByParameterName = FALSE, consoleOutputEnabled = consoleOutputEnabled
)
.showParametersOfOneGroup(.getGeneratedParameters(),
ifelse(identical(.objectType, "sampleSize"), "Sample size and output", "Power and output"),
orderByParameterName = FALSE, consoleOutputEnabled = consoleOutputEnabled
)
.showUnknownParameters(consoleOutputEnabled = consoleOutputEnabled)
if (inherits(.self, "TrialDesignPlanSurvival") || groups == 2 || .design$kMax > 1) {
.cat("Legend:\n",
heading = 2,
consoleOutputEnabled = consoleOutputEnabled
)
if (inherits(.self, "TrialDesignPlanSurvival") || groups == 2) {
.cat(" (i): values of treatment arm i\n",
consoleOutputEnabled = consoleOutputEnabled
)
}
if (.design$kMax > 1) {
.cat(" [k]: values at stage k\n", consoleOutputEnabled = consoleOutputEnabled)
}
}
.cat("\n", consoleOutputEnabled = consoleOutputEnabled)
}
},
getAlpha = function() {
return(.design$alpha)
},
getBeta = function() {
if (.isTrialDesignInverseNormalOrGroupSequential(.design)) {
return(.design$beta)
}
return(NA_real_)
},
getSided = function() {
return(.design$sided)
},
getTwoSidedPower = function() {
if (.isTrialDesignInverseNormalOrGroupSequential(.design)) {
return(.design$twoSidedPower)
}
return(NA)
},
.toString = function(startWithUpperCase = FALSE) {
if (.isTrialDesignPlanMeans(.self)) {
s <- "means"
} else if (.isTrialDesignPlanRates(.self)) {
s <- "rates"
} else if (.isTrialDesignPlanSurvival(.self)) {
s <- "survival data"
} else {
s <- paste0("unknown data class '", .getClassName(.self), "'")
}
return(ifelse(startWithUpperCase, .firstCharacterToUpperCase(s), s))
}
)
)
#'
#' @title
#' Coerce Trial Design Plan to a Data Frame
#'
#' @description
#' Returns the \code{\link{TrialDesignPlan}} as data frame.
#'
#' @param x A \code{\link{TrialDesignPlan}} object.
#' @inheritParams param_niceColumnNamesEnabled
#' @inheritParams param_includeAllParameters
#' @inheritParams param_three_dots
#'
#' @details
#' Coerces the design plan to a data frame.
#'
#' @template return_dataframe
#'
#' @examples
#' as.data.frame(getSampleSizeMeans())
#'
#' @export
#'
#' @keywords internal
#'
as.data.frame.TrialDesignPlan <- function(x, row.names = NULL,
optional = FALSE, niceColumnNamesEnabled = FALSE, includeAllParameters = FALSE, ...) {
return(.getAsDataFrame(
parameterSet = x,
parameterNames = NULL,
niceColumnNamesEnabled = niceColumnNamesEnabled,
includeAllParameters = includeAllParameters
))
}
#'
#' @name TrialDesignPlanMeans
#'
#' @title
#' Trial Design Plan Means
#'
#' @description
#' Trial design plan for means.
#'
#' @template field_meanRatio
#' @template field_thetaH0
#' @template field_normalApproximation
#' @template field_alternative
#' @template field_stDev
#' @template field_groups
#' @template field_allocationRatioPlanned
#' @template field_optimumAllocationRatio
#' @template field_directionUpper
#' @template field_effect
#' @template field_overallReject
#' @template field_rejectPerStage
#' @template field_futilityStop
#' @template field_futilityPerStage
#' @template field_earlyStop
#' @template field_expectedNumberOfSubjects
#' @template field_nFixed
#' @template field_nFixed1
#' @template field_nFixed2
#' @template field_informationRates
#' @template field_maxNumberOfSubjects
#' @template field_maxNumberOfSubjects1
#' @template field_maxNumberOfSubjects2
#' @template field_numberOfSubjects
#' @template field_numberOfSubjects1
#' @template field_numberOfSubjects2
#' @template field_expectedNumberOfSubjectsH0
#' @template field_expectedNumberOfSubjectsH01
#' @template field_expectedNumberOfSubjectsH1
#' @template field_criticalValuesEffectScale
#' @template field_criticalValuesEffectScaleLower
#' @template field_criticalValuesEffectScaleUpper
#' @template field_criticalValuesPValueScale
#' @template field_futilityBoundsEffectScale
#' @template field_futilityBoundsEffectScaleLower
#' @template field_futilityBoundsEffectScaleUpper
#' @template field_futilityBoundsPValueScale
#'
#' @details
#' This object cannot be created directly; use \code{\link[=getSampleSizeMeans]{getSampleSizeMeans()}}
#' with suitable arguments to create a design plan for a dataset of means.
#'
#' @include class_core_parameter_set.R
#' @include class_design.R
#' @include class_design_set.R
#' @include f_core_constants.R
#'
#' @keywords internal
#'
#' @importFrom methods new
#'
TrialDesignPlanMeans <- setRefClass("TrialDesignPlanMeans",
contains = "TrialDesignPlan",
fields = list(
meanRatio = "logical",
thetaH0 = "numeric",
normalApproximation = "logical",
alternative = "numeric",
stDev = "numeric",
groups = "numeric",
allocationRatioPlanned = "numeric",
optimumAllocationRatio = "logical",
directionUpper = "logical",
effect = "numeric",
maxNumberOfSubjects = "numeric",
maxNumberOfSubjects1 = "numeric",
maxNumberOfSubjects2 = "numeric",
numberOfSubjects = "matrix",
numberOfSubjects1 = "matrix",
numberOfSubjects2 = "matrix",
overallReject = "numeric",
rejectPerStage = "matrix",
futilityStop = "numeric",
futilityPerStage = "matrix",
earlyStop = "numeric",
expectedNumberOfSubjects = "numeric",
nFixed = "numeric",
nFixed1 = "numeric",
nFixed2 = "numeric",
informationRates = "matrix",
expectedNumberOfSubjectsH0 = "numeric",
expectedNumberOfSubjectsH01 = "numeric",
expectedNumberOfSubjectsH1 = "numeric",
criticalValuesEffectScale = "matrix",
criticalValuesEffectScaleLower = "matrix",
criticalValuesEffectScaleUpper = "matrix",
criticalValuesPValueScale = "matrix",
futilityBoundsEffectScale = "matrix",
futilityBoundsEffectScaleLower = "matrix",
futilityBoundsEffectScaleUpper = "matrix",
futilityBoundsPValueScale = "matrix"
),
methods = list(
initialize = function(...,
normalApproximation = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_MEANS[["normalApproximation"]],
meanRatio = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_MEANS[["meanRatio"]],
thetaH0 = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_MEANS[["thetaH0"]],
alternative = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_MEANS[["alternative"]],
stDev = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_MEANS[["stDev"]],
groups = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_MEANS[["groups"]],
allocationRatioPlanned = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_MEANS[["allocationRatioPlanned"]]) {
callSuper(...,
normalApproximation = normalApproximation,
meanRatio = meanRatio,
thetaH0 = thetaH0,
alternative = alternative,
stDev = stDev,
groups = groups,
allocationRatioPlanned = allocationRatioPlanned
)
optimumAllocationRatio <<- FALSE
visibleFieldNames <- .getVisibleFieldNames()
startIndex <- which(visibleFieldNames == "directionUpper")
for (i in startIndex:length(visibleFieldNames)) {
.setParameterType(visibleFieldNames[i], C_PARAM_NOT_APPLICABLE)
}
if (groups == 1) {
.setParameterType("meanRatio", C_PARAM_NOT_APPLICABLE)
.setParameterType("allocationRatioPlanned", C_PARAM_NOT_APPLICABLE)
}
.setParameterType("maxNumberOfSubjects1", C_PARAM_NOT_APPLICABLE)
.setParameterType("maxNumberOfSubjects2", C_PARAM_NOT_APPLICABLE)
.setParameterType("criticalValuesEffectScale", C_PARAM_NOT_APPLICABLE)
.setParameterType("criticalValuesEffectScaleLower", C_PARAM_NOT_APPLICABLE)
.setParameterType("criticalValuesEffectScaleUpper", C_PARAM_NOT_APPLICABLE)
.setParameterType("futilityBoundsEffectScale", C_PARAM_NOT_APPLICABLE)
.setParameterType("futilityBoundsEffectScaleLower", C_PARAM_NOT_APPLICABLE)
.setParameterType("futilityBoundsEffectScaleUpper", C_PARAM_NOT_APPLICABLE)
},
clone = function(alternative = NA_real_) {
alternativeTemp <- alternative
if (any(is.na(alternative))) {
alternativeTemp <- .self$alternative
}
if (.objectType == "sampleSize") {
result <- getSampleSizeMeans(
design = .self$.design,
normalApproximation = .self$.getParameterValueIfUserDefinedOrDefault("normalApproximation"),
meanRatio = .self$meanRatio, # .getParameterValueIfUserDefinedOrDefault("meanRatio"),
thetaH0 = .self$.getParameterValueIfUserDefinedOrDefault("thetaH0"),
alternative = alternativeTemp,
stDev = .self$.getParameterValueIfUserDefinedOrDefault("stDev"),
groups = .self$.getParameterValueIfUserDefinedOrDefault("groups"),
allocationRatioPlanned = .self$.getParameterValueIfUserDefinedOrDefault("allocationRatioPlanned")
)
} else {
result <- getPowerMeans(
design = .self$.design,
normalApproximation = .self$.getParameterValueIfUserDefinedOrDefault("normalApproximation"),
meanRatio = .self$meanRatio, # .getParameterValueIfUserDefinedOrDefault("meanRatio"),
thetaH0 = .self$.getParameterValueIfUserDefinedOrDefault("thetaH0"),
alternative = alternativeTemp,
stDev = .self$.getParameterValueIfUserDefinedOrDefault("stDev"),
directionUpper = .self$.getParameterValueIfUserDefinedOrDefault("directionUpper"),
maxNumberOfSubjects = .self$.getParameterValueIfUserDefinedOrDefault("maxNumberOfSubjects"),
groups = .self$.getParameterValueIfUserDefinedOrDefault("groups"),
allocationRatioPlanned = .self$.getParameterValueIfUserDefinedOrDefault("allocationRatioPlanned")
)
}
result$.plotSettings <- .self$.plotSettings
return(result)
},
show = function(showType = 1, digits = NA_integer_) {
"Method for automatically printing trial plan objects"
callSuper(showType = showType, digits = digits)
}
)
)
#'
#' @name TrialDesignPlanRates
#'
#' @title
#' Trial Design Plan Rates
#'
#' @description
#' Trial design plan for rates.
#'
#' @template field_riskRatio
#' @template field_thetaH0
#' @template field_normalApproximation
#' @template field_pi1
#' @template field_pi2
#' @template field_groups
#' @template field_allocationRatioPlanned
#' @template field_optimumAllocationRatio
#' @template field_directionUpper
#' @template field_effect
#' @template field_overallReject
#' @template field_rejectPerStage
#' @template field_futilityStop
#' @template field_futilityPerStage
#' @template field_earlyStop
#' @template field_expectedNumberOfSubjects
#' @template field_nFixed
#' @template field_nFixed1
#' @template field_nFixed2
#' @template field_informationRates
#' @template field_maxNumberOfSubjects
#' @template field_maxNumberOfSubjects1
#' @template field_maxNumberOfSubjects2
#' @template field_numberOfSubjects
#' @template field_numberOfSubjects1
#' @template field_numberOfSubjects2
#' @template field_expectedNumberOfSubjectsH0
#' @template field_expectedNumberOfSubjectsH01
#' @template field_expectedNumberOfSubjectsH1
#' @template field_criticalValuesEffectScale
#' @template field_criticalValuesEffectScaleLower
#' @template field_criticalValuesEffectScaleUpper
#' @template field_criticalValuesPValueScale
#' @template field_futilityBoundsEffectScale
#' @template field_futilityBoundsEffectScaleLower
#' @template field_futilityBoundsEffectScaleUpper
#' @template field_futilityBoundsPValueScale
#'
#' @details
#' This object cannot be created directly; use \code{\link[=getSampleSizeRates]{getSampleSizeRates()}}
#' with suitable arguments to create a design plan for a dataset of rates.
#'
#' @include class_core_parameter_set.R
#' @include class_design.R
#' @include class_design_set.R
#' @include f_core_constants.R
#'
#' @keywords internal
#'
#' @importFrom methods new
#'
TrialDesignPlanRates <- setRefClass("TrialDesignPlanRates",
contains = "TrialDesignPlan",
fields = list(
riskRatio = "logical",
thetaH0 = "numeric",
normalApproximation = "logical",
pi1 = "numeric",
pi2 = "numeric",
groups = "numeric",
allocationRatioPlanned = "numeric",
optimumAllocationRatio = "logical",
directionUpper = "logical",
effect = "numeric",
maxNumberOfSubjects = "numeric",
maxNumberOfSubjects1 = "numeric",
maxNumberOfSubjects2 = "numeric",
numberOfSubjects = "matrix",
numberOfSubjects1 = "matrix",
numberOfSubjects2 = "matrix",
overallReject = "numeric",
rejectPerStage = "matrix",
futilityStop = "numeric",
futilityPerStage = "matrix",
earlyStop = "numeric",
expectedNumberOfSubjects = "numeric",
nFixed = "numeric",
nFixed1 = "numeric",
nFixed2 = "numeric",
informationRates = "matrix",
expectedNumberOfSubjectsH0 = "numeric",
expectedNumberOfSubjectsH01 = "numeric",
expectedNumberOfSubjectsH1 = "numeric",
criticalValuesEffectScale = "matrix",
criticalValuesEffectScaleLower = "matrix",
criticalValuesEffectScaleUpper = "matrix",
criticalValuesPValueScale = "matrix",
futilityBoundsEffectScale = "matrix",
futilityBoundsEffectScaleLower = "matrix",
futilityBoundsEffectScaleUpper = "matrix",
futilityBoundsPValueScale = "matrix"
),
methods = list(
initialize = function(...,
normalApproximation = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_RATES[["normalApproximation"]],
riskRatio = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_RATES[["riskRatio"]],
thetaH0 = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_RATES[["thetaH0"]],
pi1 = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_RATES[["pi1"]],
pi2 = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_RATES[["pi2"]],
groups = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_RATES[["groups"]],
allocationRatioPlanned = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_RATES[["allocationRatioPlanned"]]) {
callSuper(...,
normalApproximation = normalApproximation,
riskRatio = riskRatio,
thetaH0 = thetaH0,
pi1 = pi1,
pi2 = pi2,
groups = groups,
allocationRatioPlanned = allocationRatioPlanned
)
optimumAllocationRatio <<- FALSE
visibleFieldNames <- .getVisibleFieldNames()
startIndex <- which(visibleFieldNames == "directionUpper")
for (i in startIndex:length(visibleFieldNames)) {
.setParameterType(visibleFieldNames[i], C_PARAM_NOT_APPLICABLE)
}
if (groups == 1) {
.setParameterType("meanRatio", C_PARAM_NOT_APPLICABLE)
.setParameterType("allocationRatioPlanned", C_PARAM_NOT_APPLICABLE)
}
.setParameterType("maxNumberOfSubjects1", C_PARAM_NOT_APPLICABLE)
.setParameterType("maxNumberOfSubjects2", C_PARAM_NOT_APPLICABLE)
.setParameterType("criticalValuesEffectScale", C_PARAM_NOT_APPLICABLE)
.setParameterType("criticalValuesEffectScaleLower", C_PARAM_NOT_APPLICABLE)
.setParameterType("criticalValuesEffectScaleUpper", C_PARAM_NOT_APPLICABLE)
.setParameterType("futilityBoundsEffectScale", C_PARAM_NOT_APPLICABLE)
.setParameterType("futilityBoundsEffectScaleLower", C_PARAM_NOT_APPLICABLE)
.setParameterType("futilityBoundsEffectScaleUpper", C_PARAM_NOT_APPLICABLE)
},
clone = function(pi1 = NA_real_) {
pi1Temp <- pi1
if (any(is.na(pi1))) {
pi1Temp <- .self$pi1
}
if (.objectType == "sampleSize") {
return(getSampleSizeRates(
design = .self$.design,
normalApproximation = .self$.getParameterValueIfUserDefinedOrDefault("normalApproximation"),
riskRatio = .self$riskRatio, # .getParameterValueIfUserDefinedOrDefault("riskRatio"),
thetaH0 = .self$.getParameterValueIfUserDefinedOrDefault("thetaH0"),
pi1 = pi1Temp,
pi2 = .self$.getParameterValueIfUserDefinedOrDefault("pi2"),
groups = .self$.getParameterValueIfUserDefinedOrDefault("groups"),
allocationRatioPlanned = .self$.getParameterValueIfUserDefinedOrDefault("allocationRatioPlanned")
))
} else {
return(getPowerRates(
design = .self$.design,
riskRatio = .self$riskRatio, # .getParameterValueIfUserDefinedOrDefault("riskRatio"),
thetaH0 = .self$.getParameterValueIfUserDefinedOrDefault("thetaH0"),
pi1 = pi1Temp,
pi2 = .self$.getParameterValueIfUserDefinedOrDefault("pi2"),
directionUpper = .self$.getParameterValueIfUserDefinedOrDefault("directionUpper"),
maxNumberOfSubjects = .self$.getParameterValueIfUserDefinedOrDefault("maxNumberOfSubjects"),
groups = .self$.getParameterValueIfUserDefinedOrDefault("groups"),
allocationRatioPlanned = .self$.getParameterValueIfUserDefinedOrDefault("allocationRatioPlanned")
))
}
},
show = function(showType = 1, digits = NA_integer_) {
"Method for automatically printing trial plan objects"
callSuper(showType = showType, digits = digits)
}
)
)
#'
#' @name TrialDesignPlanSurvival
#'
#' @title
#' Trial Design Plan Survival
#'
#' @description
#' Trial design plan for survival data.
#'
#' @template field_thetaH0
#' @template field_typeOfComputation
#' @template field_directionUpper
#' @template field_pi1_survival
#' @template field_pi2_survival
#' @template field_median1
#' @template field_median2
#' @template field_lambda1
#' @template field_lambda2
#' @template field_hazardRatio
#' @template field_maxNumberOfSubjects
#' @template field_maxNumberOfSubjects1
#' @template field_maxNumberOfSubjects2
#' @template field_maxNumberOfEvents
#' @template field_allocationRatioPlanned
#' @template field_optimumAllocationRatio
#' @template field_accountForObservationTimes
#' @template field_eventTime
#' @template field_accrualTime
#' @template field_totalAccrualTime
#' @template field_accrualIntensity
#' @template field_accrualIntensityRelative
#' @template field_kappa
#' @template field_piecewiseSurvivalTime
#' @template field_followUpTime
#' @template field_dropoutRate1
#' @template field_dropoutRate2
#' @template field_dropoutTime
#' @template field_chi
#' @template field_expectedNumberOfEvents
#' @template field_eventsFixed
#' @template field_nFixed
#' @template field_nFixed1
#' @template field_nFixed2
#' @template field_overallReject
#' @template field_rejectPerStage
#' @template field_futilityStop
#' @template field_futilityPerStage
#' @template field_earlyStop
#' @template field_informationRates
#' @template field_analysisTime
#' @template field_studyDurationH1
#' @template field_studyDuration
#' @template field_maxStudyDuration
#' @template field_eventsPerStage
#' @template field_singleEventsPerStage
#' @template field_cumulativeEventsPerStage
#' @template field_expectedEventsH0
#' @template field_expectedEventsH01
#' @template field_expectedEventsH1
#' @template field_numberOfSubjects
#' @template field_numberOfSubjects1
#' @template field_numberOfSubjects2
#' @template field_expectedNumberOfSubjectsH1
#' @template field_expectedNumberOfSubjects
#' @template field_criticalValuesEffectScale
#' @template field_criticalValuesEffectScaleLower
#' @template field_criticalValuesEffectScaleUpper
#' @template field_criticalValuesPValueScale
#' @template field_futilityBoundsEffectScale
#' @template field_futilityBoundsEffectScaleLower
#' @template field_futilityBoundsEffectScaleUpper
#' @template field_futilityBoundsPValueScale
#'
#' @details
#' This object cannot be created directly; use \code{\link[=getSampleSizeSurvival]{getSampleSizeSurvival()}}
#' with suitable arguments to create a design plan for a dataset of survival data.
#'
#' @include class_core_parameter_set.R
#' @include class_design.R
#' @include class_design_set.R
#' @include class_time.R
#' @include f_core_constants.R
#'
#' @keywords internal
#'
#' @importFrom methods new
#'
TrialDesignPlanSurvival <- setRefClass("TrialDesignPlanSurvival",
contains = "TrialDesignPlan",
fields = list(
.piecewiseSurvivalTime = "PiecewiseSurvivalTime",
.accrualTime = "AccrualTime",
.calculateFollowUpTime = "logical",
thetaH0 = "numeric",
typeOfComputation = "character",
directionUpper = "logical",
pi1 = "numeric",
pi2 = "numeric",
median1 = "numeric",
median2 = "numeric",
lambda1 = "numeric",
lambda2 = "numeric",
hazardRatio = "numeric",
maxNumberOfSubjects = "numeric",
maxNumberOfSubjects1 = "numeric",
maxNumberOfSubjects2 = "numeric",
numberOfSubjects = "matrix",
numberOfSubjects1 = "matrix",
numberOfSubjects2 = "matrix",
maxNumberOfEvents = "numeric",
allocationRatioPlanned = "numeric",
optimumAllocationRatio = "logical",
accountForObservationTimes = "logical",
eventTime = "numeric",
accrualTime = "numeric",
totalAccrualTime = "numeric",
accrualIntensity = "numeric",
accrualIntensityRelative = "numeric",
kappa = "numeric",
piecewiseSurvivalTime = "numeric",
followUpTime = "numeric",
dropoutRate1 = "numeric",
dropoutRate2 = "numeric",
dropoutTime = "numeric",
chi = "numeric",
expectedNumberOfEvents = "numeric",
eventsFixed = "numeric",
nFixed = "numeric",
nFixed1 = "numeric",
nFixed2 = "numeric",
overallReject = "numeric",
rejectPerStage = "matrix",
futilityStop = "numeric",
futilityPerStage = "matrix",
earlyStop = "numeric",
informationRates = "matrix",
analysisTime = "matrix",
studyDurationH1 = "numeric",
studyDuration = "numeric",
maxStudyDuration = "numeric",
eventsPerStage = "matrix", # deprecated
singleEventsPerStage = "matrix",
cumulativeEventsPerStage = "matrix",
expectedEventsH0 = "numeric",
expectedEventsH01 = "numeric",
expectedEventsH1 = "numeric",
expectedNumberOfSubjectsH1 = "numeric",
expectedNumberOfSubjects = "numeric",
criticalValuesEffectScale = "matrix",
criticalValuesEffectScaleLower = "matrix",
criticalValuesEffectScaleUpper = "matrix",
criticalValuesPValueScale = "matrix",
futilityBoundsEffectScale = "matrix",
futilityBoundsEffectScaleLower = "matrix",
futilityBoundsEffectScaleUpper = "matrix",
futilityBoundsPValueScale = "matrix"
),
methods = list(
initialize = function(...) {
callSuper(...)
optimumAllocationRatio <<- FALSE
visibleFieldNames <- .getVisibleFieldNames()
startIndex <- which(visibleFieldNames == "hazardRatio")
for (i in startIndex:length(visibleFieldNames)) {
.setParameterType(visibleFieldNames[i], C_PARAM_NOT_APPLICABLE)
}
.setParameterType("maxNumberOfSubjects", C_PARAM_NOT_APPLICABLE)
.setParameterType("maxNumberOfSubjects1", C_PARAM_NOT_APPLICABLE)
.setParameterType("maxNumberOfSubjects2", C_PARAM_NOT_APPLICABLE)
.setParameterType("median1", C_PARAM_NOT_APPLICABLE)
.setParameterType("median2", C_PARAM_NOT_APPLICABLE)
.setParameterType("accountForObservationTimes", C_PARAM_NOT_APPLICABLE)
.setParameterType("chi", C_PARAM_NOT_APPLICABLE)
.setParameterType("maxStudyDuration", C_PARAM_NOT_APPLICABLE)
.setParameterType("accrualIntensityRelative", C_PARAM_NOT_APPLICABLE)
.setParameterType("criticalValuesEffectScale", C_PARAM_NOT_APPLICABLE)
.setParameterType("criticalValuesEffectScaleLower", C_PARAM_NOT_APPLICABLE)
.setParameterType("criticalValuesEffectScaleUpper", C_PARAM_NOT_APPLICABLE)
.setParameterType("futilityBoundsEffectScale", C_PARAM_NOT_APPLICABLE)
.setParameterType("futilityBoundsEffectScaleLower", C_PARAM_NOT_APPLICABLE)
.setParameterType("futilityBoundsEffectScaleUpper", C_PARAM_NOT_APPLICABLE)
.setParameterType("singleEventsPerStage", C_PARAM_NOT_APPLICABLE)
# set default values
for (parameterName in c(
"eventTime", "accrualTime", "accrualIntensity",
"kappa", "piecewiseSurvivalTime", "lambda1", "lambda2",
"followUpTime", "dropoutTime"
)) {
.setDefaultValue(parameterName)
}
},
clone = function(hazardRatio = NA_real_, pi1 = NA_real_) {
hr <- NA_real_
if (.getParameterType("hazardRatio") == C_PARAM_USER_DEFINED) {
hr <- hazardRatio
if (any(is.na(hazardRatio))) {
hr <- .self$hazardRatio
}
}
pi1Temp <- NA_real_
if (.getParameterType("pi1") == C_PARAM_USER_DEFINED) {
pi1Temp <- pi1
if (any(is.na(pi1))) {
pi1Temp <- .self$pi1
}
}
accrualTimeTemp <- .self$.getParameterValueIfUserDefinedOrDefault("accrualTime")
if (!is.null(accrualTimeTemp) && length(accrualTimeTemp) > 0 &&
!all(is.na(accrualTimeTemp)) && accrualTimeTemp[1] != 0) {
accrualTimeTemp <- c(0, accrualTimeTemp)
}
accrualIntensityTemp <- .self$.getParameterValueIfUserDefinedOrDefault("accrualIntensity")
if (all(is.na(accrualIntensityTemp))) {
accrualIntensityTemp <- C_ACCRUAL_INTENSITY_DEFAULT
}
if (.objectType == "sampleSize") {
return(getSampleSizeSurvival(
design = .self$.design,
typeOfComputation = .self$.getParameterValueIfUserDefinedOrDefault("typeOfComputation"),
thetaH0 = .self$.getParameterValueIfUserDefinedOrDefault("thetaH0"),
pi1 = pi1Temp,
pi2 = .self$.getParameterValueIfUserDefinedOrDefault("pi2"),
allocationRatioPlanned = .self$allocationRatioPlanned,
accountForObservationTimes = .self$.getParameterValueIfUserDefinedOrDefault("accountForObservationTimes"),
eventTime = .self$eventTime,
accrualTime = accrualTimeTemp,
accrualIntensity = accrualIntensityTemp,
kappa = .self$kappa,
piecewiseSurvivalTime = .self$.getParameterValueIfUserDefinedOrDefault("piecewiseSurvivalTime"),
lambda2 = .self$.getParameterValueIfUserDefinedOrDefault("lambda2"),
lambda1 = .self$.getParameterValueIfUserDefinedOrDefault("lambda1"),
followUpTime = .self$.getParameterValueIfUserDefinedOrDefault("followUpTime"),
maxNumberOfSubjects = .self$.getParameterValueIfUserDefinedOrDefault("maxNumberOfSubjects"),
dropoutRate1 = .self$dropoutRate1,
dropoutRate2 = .self$dropoutRate2,
dropoutTime = .self$dropoutTime,
hazardRatio = hr
))
} else {
directionUpperTemp <- directionUpper
if (length(directionUpperTemp) > 1) {
directionUpperTemp <- directionUpperTemp[1]
}
return(getPowerSurvival(
design = .self$.design,
typeOfComputation = .self$.getParameterValueIfUserDefinedOrDefault("typeOfComputation"),
thetaH0 = .self$.getParameterValueIfUserDefinedOrDefault("thetaH0"),
pi1 = pi1Temp,
pi2 = .self$.getParameterValueIfUserDefinedOrDefault("pi2"),
directionUpper = directionUpperTemp,
allocationRatioPlanned = .self$allocationRatioPlanned,
eventTime = .self$eventTime,
accrualTime = accrualTimeTemp,
accrualIntensity = accrualIntensityTemp,
kappa = .self$kappa,
piecewiseSurvivalTime = .self$.getParameterValueIfUserDefinedOrDefault("piecewiseSurvivalTime"),
lambda2 = .self$.getParameterValueIfUserDefinedOrDefault("lambda2"),
lambda1 = .self$.getParameterValueIfUserDefinedOrDefault("lambda1"),
hazardRatio = hr,
maxNumberOfSubjects = .self$.getParameterValueIfUserDefinedOrDefault("maxNumberOfSubjects"),
maxNumberOfEvents = .self$.getParameterValueIfUserDefinedOrDefault("maxNumberOfEvents"),
dropoutRate1 = .self$dropoutRate1,
dropoutRate2 = .self$dropoutRate2,
dropoutTime = .self$dropoutTime
))
}
},
.setDefaultValue = function(argumentName) {
if (is.null(.self[[argumentName]]) || all(is.na(.self[[argumentName]]))) {
.self[[argumentName]] <<- C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_SURVIVAL[[argumentName]]
.setParameterType(argumentName, C_PARAM_DEFAULT_VALUE)
}
},
show = function(showType = 1, digits = NA_integer_) {
"Method for automatically printing trial plan objects"
callSuper(showType = showType, digits = digits)
},
.warnInCaseArgumentExists = function(argument, argumentName) {
if (!all(is.na(argument)) && any(argument > 0)) {
warning(sprintf(
"Specified '%s' (%s) not taken into account",
argumentName, .arrayToString(argument)
), call. = FALSE)
}
}
)
)
#'
#' @name TrialDesignPlanCountData
#'
#' @title
#' Trial Design Plan Count Data
#'
#' @description
#' Trial design plan for count data.
#'
#' @template field_thetaH0
#' @template field_groups
#' @template field_allocationRatioPlanned
#' @template field_optimumAllocationRatio
#' @template field_directionUpper
#' @template field_lambda1
#' @template field_lambda2
#' @template field_lambda
#' @template field_theta
#' @template field_nFixed
#' @template field_nFixed1
#' @template field_nFixed2
#' @template field_maxNumberOfSubjects
#' @template field_maxNumberOfSubjects1
#' @template field_maxNumberOfSubjects2
#' @template field_overallReject
#' @template field_rejectPerStage
#' @template field_futilityStop
#' @template field_futilityPerStage
#' @template field_earlyStop
#' @template field_overdispersion
#' @template field_fixedExposureTime
#' @template field_accrualTime
#' @template field_accrualIntensity
#' @template field_followUpTime
#' @template field_calendarTime
#' @template field_expectedStudyDurationH1
#' @template field_studyTime
#' @template field_numberOfSubjects
#' @template field_expectedNumberOfSubjectsH1
#' @template field_informationOverStages
#' @template field_expectedInformationH0
#' @template field_expectedInformationH01
#' @template field_expectedInformationH1
#' @template field_maxInformation
#' @template field_futilityBoundsPValueScale
#'
#' @details
#' This object cannot be created directly; use \code{\link[=getSampleSizeCounts]{getSampleSizeCounts()}}
#' with suitable arguments to create a design plan for a dataset of rates.
#'
#' @include class_core_parameter_set.R
#' @include class_design.R
#' @include class_design_set.R
#' @include f_core_constants.R
#'
#' @keywords internal
#'
#' @importFrom methods new
#'
TrialDesignPlanCountData <- setRefClass("TrialDesignPlanCountData",
contains = "TrialDesignPlan",
fields = list(
.designCharacteristics = "ANY",
thetaH0 = "numeric",
groups = "integer",
allocationRatioPlanned = "numeric",
optimumAllocationRatio = "logical",
directionUpper = "logical",
lambda1 = "numeric",
lambda2 = "numeric",
lambda = "numeric",
theta = "numeric",
nFixed = "numeric",
nFixed1 = "numeric",
nFixed2 = "numeric",
maxNumberOfSubjects = "numeric",
maxNumberOfSubjects1 = "numeric",
maxNumberOfSubjects2 = "numeric",
overallReject = "numeric",
rejectPerStage = "matrix",
futilityStop = "numeric",
futilityPerStage = "matrix",
earlyStop = "numeric",
overdispersion = "numeric",
fixedExposureTime = "numeric",
accrualTime = "numeric",
accrualIntensity = "numeric",
followUpTime = "numeric",
calendarTime = "matrix",
expectedStudyDurationH1 = "numeric",
studyTime = "numeric",
numberOfSubjects = "matrix",
expectedNumberOfSubjectsH1 = "numeric",
informationOverStages = "matrix",
expectedInformationH0 = "numeric",
expectedInformationH01 = "numeric",
expectedInformationH1 = "numeric",
maxInformation = "numeric",
futilityBoundsPValueScale = "matrix"
),
methods = list(
initialize = function(...,
designCharacteristics,
lambda1 = NA_real_,
lambda2 = NA_real_,
lambda = NA_real_,
theta = NA_real_,
thetaH0 = 1,
overdispersion = 0,
fixedExposureTime = NA_real_,
accrualTime = NA_real_,
accrualIntensity = NA_real_,
followUpTime = NA_real_,
allocationRatioPlanned = NA_real_) {
callSuper(...,
.designCharacteristics = designCharacteristics,
lambda1 = lambda1,
lambda2 = lambda2,
lambda = lambda,
theta = theta,
thetaH0 = thetaH0,
overdispersion = overdispersion,
fixedExposureTime = fixedExposureTime,
accrualTime = accrualTime,
accrualIntensity = accrualIntensity,
followUpTime = followUpTime,
allocationRatioPlanned = allocationRatioPlanned
)
groups <<- 2L
optimumAllocationRatio <<- FALSE
.self$.setParameterType("groups", C_PARAM_NOT_APPLICABLE)
.self$.setParameterType("directionUpper", C_PARAM_NOT_APPLICABLE)
.self$.setParameterType("optimumAllocationRatio", C_PARAM_NOT_APPLICABLE)
},
.toString = function(startWithUpperCase = FALSE) {
s <- "count data"
return(ifelse(startWithUpperCase, .firstCharacterToUpperCase(s), s))
},
clone = function(..., lambda1 = NA_real_, theta = NA_real_) {
if (all(is.na(lambda1))) {
lambda1Temp <- .self$.getParameterValueIfUserDefinedOrDefault("lambda1")
} else {
lambda1Temp <- lambda1
if (any(is.na(lambda1))) {
lambda1Temp <- .self$lambda1
}
}
if (all(is.na(theta))) {
thetaTemp <- .self$.getParameterValueIfUserDefinedOrDefault("theta")
} else {
thetaTemp <- theta
if (any(is.na(theta))) {
thetaTemp <- .self$theta
}
}
if (.objectType == "sampleSize") {
result <- getSampleSizeCounts(
design = .self$.design,
lambda1 = lambda1Temp,
lambda2 = .self$.getParameterValueIfUserDefinedOrDefault("lambda2"),
lambda = .self$.getParameterValueIfUserDefinedOrDefault("lambda"),
theta = thetaTemp,
thetaH0 = .self$.getParameterValueIfUserDefinedOrDefault("thetaH0"),
overdispersion = .self$.getParameterValueIfUserDefinedOrDefault("overdispersion"),
fixedExposureTime = .self$.getParameterValueIfUserDefinedOrDefault("fixedExposureTime"),
accrualTime = .self$.getParameterValueIfUserDefinedOrDefault("accrualTime"),
accrualIntensity = .self$.getParameterValueIfUserDefinedOrDefault("accrualIntensity"),
followUpTime = .self$.getParameterValueIfUserDefinedOrDefault("followUpTime"),
maxNumberOfSubjects = .self$.getParameterValueIfUserDefinedOrDefault("maxNumberOfSubjects"),
allocationRatioPlanned = .self$.getParameterValueIfUserDefinedOrDefault("allocationRatioPlanned")
)
} else {
result <- getPowerCounts(
design = .self$.design,
directionUpper = .self$.getParameterValueIfUserDefinedOrDefault("directionUpper"),
maxNumberOfSubjects = .self$.getParameterValueIfUserDefinedOrDefault("maxNumberOfSubjects"),
lambda1 = lambda1Temp,
lambda2 = .self$.getParameterValueIfUserDefinedOrDefault("lambda2"),
lambda = .self$.getParameterValueIfUserDefinedOrDefault("lambda"),
theta = thetaTemp,
thetaH0 = .self$.getParameterValueIfUserDefinedOrDefault("thetaH0"),
overdispersion = .self$.getParameterValueIfUserDefinedOrDefault("overdispersion"),
fixedExposureTime = .self$.getParameterValueIfUserDefinedOrDefault("fixedExposureTime"),
accrualTime = .self$.getParameterValueIfUserDefinedOrDefault("accrualTime"),
accrualIntensity = .self$.getParameterValueIfUserDefinedOrDefault("accrualIntensity"),
followUpTime = .self$.getParameterValueIfUserDefinedOrDefault("followUpTime"),
allocationRatioPlanned = .self$.getParameterValueIfUserDefinedOrDefault("allocationRatioPlanned")
)
}
result$.plotSettings <- .self$.plotSettings
return(result)
}
)
)
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Defines functions as.data.frame.TrialDesignPlan
Documented in as.data.frame.TrialDesignPlan
## |
## | *Trial design plan classes*
## |
## | 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: 7651 $
## | Last changed: $Date: 2024-02-20 15:45:44 +0100 (Di, 20 Feb 2024) $
## | Last changed by: $Author: pahlke $
## |
#' @include f_core_constants.R
#' @include f_design_general_utilities.R
NULL
C_VARIABLE_DESIGN_PLAN_PARAMETERS <- c("lambda1", "pi1", "median1", "alternative", "hazardRatio", "theta")
C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_MEANS <- list(
normalApproximation = FALSE,
meanRatio = FALSE,
thetaH0 = 0,
alternative = seq(0.2, 1, 0.2),
stDev = 1,
groups = 2L,
allocationRatioPlanned = 1
)
C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_RATES <- list(
normalApproximation = TRUE,
riskRatio = FALSE,
thetaH0 = 0,
pi1 = C_PI_1_SAMPLE_SIZE_DEFAULT,
pi2 = C_PI_2_DEFAULT,
groups = 2L,
allocationRatioPlanned = 1
)
C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_SURVIVAL <- list(
typeOfComputation = "Schoenfeld",
thetaH0 = 1,
pi2 = C_PI_2_DEFAULT,
pi1 = C_PI_1_SAMPLE_SIZE_DEFAULT,
allocationRatioPlanned = 1,
accountForObservationTimes = NA,
eventTime = 12,
accrualTime = C_ACCRUAL_TIME_DEFAULT,
accrualIntensity = C_ACCRUAL_INTENSITY_DEFAULT,
kappa = 1,
piecewiseSurvivalTime = NA_real_,
lambda2 = NA_real_,
lambda1 = NA_real_,
followUpTime = C_FOLLOW_UP_TIME_DEFAULT,
maxNumberOfSubjects = 0,
dropoutRate1 = 0,
dropoutRate2 = 0,
dropoutTime = 12
)
C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_COUNT_DATA <- list(
thetaH0 = 1,
overdispersion = 0,
allocationRatioPlanned = 1
)
#'
#' @name TrialDesignPlan
#'
#' @title
#' Basic Trial Design Plan
#'
#' @description
#' Basic class for trial design plans.
#'
#' @details
#' \code{TrialDesignPlan} is the basic class for
#' \itemize{
#' \item \code{\link{TrialDesignPlanMeans}},
#' \item \code{\link{TrialDesignPlanRates}}, and
#' \item \code{\link{TrialDesignPlanSurvival}}.
#' }
#'
#' @include f_core_constants.R
#' @include f_core_utilities.R
#' @include class_core_parameter_set.R
#' @include class_core_plot_settings.R
#' @include class_design.R
#' @include class_design_set.R
#' @include f_core_plot.R
#'
#' @keywords internal
#'
#' @importFrom methods new
#'
TrialDesignPlan <- setRefClass("TrialDesignPlan",
contains = "ParameterSet",
fields = list(
.plotSettings = "PlotSettings",
.design = "TrialDesign",
.objectType = "character" # "sampleSize" or "power"
),
methods = list(
initialize = function(design, ...) {
callSuper(.design = design, ...)
.plotSettings <<- PlotSettings()
if (.isTrialDesignPlanMeans(.self)) {
defaultValueList <- C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_MEANS
} else if (.isTrialDesignPlanRates(.self)) {
defaultValueList <- C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_RATES
} else if (.isTrialDesignPlanSurvival(.self)) {
defaultValueList <- C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_SURVIVAL
} else if (.isTrialDesignPlanCountData(.self)) {
defaultValueList <- C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_COUNT_DATA
}
for (parameterName in .getVisibleFieldNames()) {
defaultValue <- defaultValueList[[parameterName]]
existingValue <- .self[[parameterName]]
if (all(is.na(existingValue))) {
.setParameterType(parameterName, C_PARAM_DEFAULT_VALUE)
} else if (!is.null(defaultValue) && length(defaultValue) == length(existingValue) &&
!any(is.na(defaultValue)) && !any(is.na(existingValue)) &&
sum(defaultValue == existingValue) == length(defaultValue)) {
.setParameterType(parameterName, C_PARAM_DEFAULT_VALUE)
} else {
.setParameterType(parameterName, C_PARAM_USER_DEFINED)
}
}
.setParameterType("optimumAllocationRatio", C_PARAM_NOT_APPLICABLE)
},
.setObjectType = function(objectType) {
if (length(objectType) == 0 || !(objectType %in% c("sampleSize", "power"))) {
stop(
C_EXCEPTION_TYPE_RUNTIME_ISSUE, "'.objectType' (", objectType,
") must be specified as 'sampleSize' or 'power'"
)
}
.objectType <<- objectType
},
.isSampleSizeObject = function() {
if (length(.objectType) == 0 || !(.objectType %in% c("sampleSize", "power"))) {
stop(C_EXCEPTION_TYPE_RUNTIME_ISSUE, "'.objectType' must be specified as 'sampleSize' or 'power'")
}
return(.objectType == "sampleSize")
},
.isPowerObject = function() {
if (length(.objectType) == 0 || !(.objectType %in% c("sampleSize", "power"))) {
stop(C_EXCEPTION_TYPE_RUNTIME_ISSUE, "'.objectType' must be specified as 'sampleSize' or 'power'")
}
return(.objectType == "power")
},
getPlotSettings = function() {
return(.plotSettings)
},
show = function(showType = 1, digits = NA_integer_) {
.show(showType = showType, digits = digits, consoleOutputEnabled = TRUE)
},
.show = function(showType = 1, digits = NA_integer_, consoleOutputEnabled = TRUE) {
"Method for automatically printing trial plan objects"
.resetCat()
if (showType == 3) {
.createSummary(.self, digits = digits)$.show(
showType = 1,
digits = digits, consoleOutputEnabled = consoleOutputEnabled
)
} else if (showType == 2) {
callSuper(showType = showType, digits = digits, consoleOutputEnabled = consoleOutputEnabled)
} else {
.cat("Design plan parameters and output for ", .toString(), ":\n\n",
heading = 1,
consoleOutputEnabled = consoleOutputEnabled
)
.showParametersOfOneGroup(.getDesignParametersToShow(.self), "Design parameters",
orderByParameterName = FALSE, consoleOutputEnabled = consoleOutputEnabled
)
.showParametersOfOneGroup(.getUserDefinedParameters(), "User defined parameters",
orderByParameterName = FALSE, consoleOutputEnabled = consoleOutputEnabled
)
.showParametersOfOneGroup(.getDefaultParameters(), "Default parameters",
orderByParameterName = FALSE, consoleOutputEnabled = consoleOutputEnabled
)
.showParametersOfOneGroup(.getGeneratedParameters(),
ifelse(identical(.objectType, "sampleSize"), "Sample size and output", "Power and output"),
orderByParameterName = FALSE, consoleOutputEnabled = consoleOutputEnabled
)
.showUnknownParameters(consoleOutputEnabled = consoleOutputEnabled)
if (inherits(.self, "TrialDesignPlanSurvival") || groups == 2 || .design$kMax > 1) {
.cat("Legend:\n",
heading = 2,
consoleOutputEnabled = consoleOutputEnabled
)
if (inherits(.self, "TrialDesignPlanSurvival") || groups == 2) {
.cat(" (i): values of treatment arm i\n",
consoleOutputEnabled = consoleOutputEnabled
)
}
if (.design$kMax > 1) {
.cat(" [k]: values at stage k\n", consoleOutputEnabled = consoleOutputEnabled)
}
}
.cat("\n", consoleOutputEnabled = consoleOutputEnabled)
}
},
getAlpha = function() {
return(.design$alpha)
},
getBeta = function() {
if (.isTrialDesignInverseNormalOrGroupSequential(.design)) {
return(.design$beta)
}
return(NA_real_)
},
getSided = function() {
return(.design$sided)
},
getTwoSidedPower = function() {
if (.isTrialDesignInverseNormalOrGroupSequential(.design)) {
return(.design$twoSidedPower)
}
return(NA)
},
.toString = function(startWithUpperCase = FALSE) {
if (.isTrialDesignPlanMeans(.self)) {
s <- "means"
} else if (.isTrialDesignPlanRates(.self)) {
s <- "rates"
} else if (.isTrialDesignPlanSurvival(.self)) {
s <- "survival data"
} else {
s <- paste0("unknown data class '", .getClassName(.self), "'")
}
return(ifelse(startWithUpperCase, .firstCharacterToUpperCase(s), s))
}
)
)
#'
#' @title
#' Coerce Trial Design Plan to a Data Frame
#'
#' @description
#' Returns the \code{\link{TrialDesignPlan}} as data frame.
#'
#' @param x A \code{\link{TrialDesignPlan}} object.
#' @inheritParams param_niceColumnNamesEnabled
#' @inheritParams param_includeAllParameters
#' @inheritParams param_three_dots
#'
#' @details
#' Coerces the design plan to a data frame.
#'
#' @template return_dataframe
#'
#' @examples
#' as.data.frame(getSampleSizeMeans())
#'
#' @export
#'
#' @keywords internal
#'
as.data.frame.TrialDesignPlan <- function(x, row.names = NULL,
optional = FALSE, niceColumnNamesEnabled = FALSE, includeAllParameters = FALSE, ...) {
return(.getAsDataFrame(
parameterSet = x,
parameterNames = NULL,
niceColumnNamesEnabled = niceColumnNamesEnabled,
includeAllParameters = includeAllParameters
))
}
#'
#' @name TrialDesignPlanMeans
#'
#' @title
#' Trial Design Plan Means
#'
#' @description
#' Trial design plan for means.
#'
#' @template field_meanRatio
#' @template field_thetaH0
#' @template field_normalApproximation
#' @template field_alternative
#' @template field_stDev
#' @template field_groups
#' @template field_allocationRatioPlanned
#' @template field_optimumAllocationRatio
#' @template field_directionUpper
#' @template field_effect
#' @template field_overallReject
#' @template field_rejectPerStage
#' @template field_futilityStop
#' @template field_futilityPerStage
#' @template field_earlyStop
#' @template field_expectedNumberOfSubjects
#' @template field_nFixed
#' @template field_nFixed1
#' @template field_nFixed2
#' @template field_informationRates
#' @template field_maxNumberOfSubjects
#' @template field_maxNumberOfSubjects1
#' @template field_maxNumberOfSubjects2
#' @template field_numberOfSubjects
#' @template field_numberOfSubjects1
#' @template field_numberOfSubjects2
#' @template field_expectedNumberOfSubjectsH0
#' @template field_expectedNumberOfSubjectsH01
#' @template field_expectedNumberOfSubjectsH1
#' @template field_criticalValuesEffectScale
#' @template field_criticalValuesEffectScaleLower
#' @template field_criticalValuesEffectScaleUpper
#' @template field_criticalValuesPValueScale
#' @template field_futilityBoundsEffectScale
#' @template field_futilityBoundsEffectScaleLower
#' @template field_futilityBoundsEffectScaleUpper
#' @template field_futilityBoundsPValueScale
#'
#' @details
#' This object cannot be created directly; use \code{\link[=getSampleSizeMeans]{getSampleSizeMeans()}}
#' with suitable arguments to create a design plan for a dataset of means.
#'
#' @include class_core_parameter_set.R
#' @include class_design.R
#' @include class_design_set.R
#' @include f_core_constants.R
#'
#' @keywords internal
#'
#' @importFrom methods new
#'
TrialDesignPlanMeans <- setRefClass("TrialDesignPlanMeans",
contains = "TrialDesignPlan",
fields = list(
meanRatio = "logical",
thetaH0 = "numeric",
normalApproximation = "logical",
alternative = "numeric",
stDev = "numeric",
groups = "numeric",
allocationRatioPlanned = "numeric",
optimumAllocationRatio = "logical",
directionUpper = "logical",
effect = "numeric",
maxNumberOfSubjects = "numeric",
maxNumberOfSubjects1 = "numeric",
maxNumberOfSubjects2 = "numeric",
numberOfSubjects = "matrix",
numberOfSubjects1 = "matrix",
numberOfSubjects2 = "matrix",
overallReject = "numeric",
rejectPerStage = "matrix",
futilityStop = "numeric",
futilityPerStage = "matrix",
earlyStop = "numeric",
expectedNumberOfSubjects = "numeric",
nFixed = "numeric",
nFixed1 = "numeric",
nFixed2 = "numeric",
informationRates = "matrix",
expectedNumberOfSubjectsH0 = "numeric",
expectedNumberOfSubjectsH01 = "numeric",
expectedNumberOfSubjectsH1 = "numeric",
criticalValuesEffectScale = "matrix",
criticalValuesEffectScaleLower = "matrix",
criticalValuesEffectScaleUpper = "matrix",
criticalValuesPValueScale = "matrix",
futilityBoundsEffectScale = "matrix",
futilityBoundsEffectScaleLower = "matrix",
futilityBoundsEffectScaleUpper = "matrix",
futilityBoundsPValueScale = "matrix"
),
methods = list(
initialize = function(...,
normalApproximation = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_MEANS[["normalApproximation"]],
meanRatio = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_MEANS[["meanRatio"]],
thetaH0 = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_MEANS[["thetaH0"]],
alternative = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_MEANS[["alternative"]],
stDev = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_MEANS[["stDev"]],
groups = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_MEANS[["groups"]],
allocationRatioPlanned = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_MEANS[["allocationRatioPlanned"]]) {
callSuper(...,
normalApproximation = normalApproximation,
meanRatio = meanRatio,
thetaH0 = thetaH0,
alternative = alternative,
stDev = stDev,
groups = groups,
allocationRatioPlanned = allocationRatioPlanned
)
optimumAllocationRatio <<- FALSE
visibleFieldNames <- .getVisibleFieldNames()
startIndex <- which(visibleFieldNames == "directionUpper")
for (i in startIndex:length(visibleFieldNames)) {
.setParameterType(visibleFieldNames[i], C_PARAM_NOT_APPLICABLE)
}
if (groups == 1) {
.setParameterType("meanRatio", C_PARAM_NOT_APPLICABLE)
.setParameterType("allocationRatioPlanned", C_PARAM_NOT_APPLICABLE)
}
.setParameterType("maxNumberOfSubjects1", C_PARAM_NOT_APPLICABLE)
.setParameterType("maxNumberOfSubjects2", C_PARAM_NOT_APPLICABLE)
.setParameterType("criticalValuesEffectScale", C_PARAM_NOT_APPLICABLE)
.setParameterType("criticalValuesEffectScaleLower", C_PARAM_NOT_APPLICABLE)
.setParameterType("criticalValuesEffectScaleUpper", C_PARAM_NOT_APPLICABLE)
.setParameterType("futilityBoundsEffectScale", C_PARAM_NOT_APPLICABLE)
.setParameterType("futilityBoundsEffectScaleLower", C_PARAM_NOT_APPLICABLE)
.setParameterType("futilityBoundsEffectScaleUpper", C_PARAM_NOT_APPLICABLE)
},
clone = function(alternative = NA_real_) {
alternativeTemp <- alternative
if (any(is.na(alternative))) {
alternativeTemp <- .self$alternative
}
if (.objectType == "sampleSize") {
result <- getSampleSizeMeans(
design = .self$.design,
normalApproximation = .self$.getParameterValueIfUserDefinedOrDefault("normalApproximation"),
meanRatio = .self$meanRatio, # .getParameterValueIfUserDefinedOrDefault("meanRatio"),
thetaH0 = .self$.getParameterValueIfUserDefinedOrDefault("thetaH0"),
alternative = alternativeTemp,
stDev = .self$.getParameterValueIfUserDefinedOrDefault("stDev"),
groups = .self$.getParameterValueIfUserDefinedOrDefault("groups"),
allocationRatioPlanned = .self$.getParameterValueIfUserDefinedOrDefault("allocationRatioPlanned")
)
} else {
result <- getPowerMeans(
design = .self$.design,
normalApproximation = .self$.getParameterValueIfUserDefinedOrDefault("normalApproximation"),
meanRatio = .self$meanRatio, # .getParameterValueIfUserDefinedOrDefault("meanRatio"),
thetaH0 = .self$.getParameterValueIfUserDefinedOrDefault("thetaH0"),
alternative = alternativeTemp,
stDev = .self$.getParameterValueIfUserDefinedOrDefault("stDev"),
directionUpper = .self$.getParameterValueIfUserDefinedOrDefault("directionUpper"),
maxNumberOfSubjects = .self$.getParameterValueIfUserDefinedOrDefault("maxNumberOfSubjects"),
groups = .self$.getParameterValueIfUserDefinedOrDefault("groups"),
allocationRatioPlanned = .self$.getParameterValueIfUserDefinedOrDefault("allocationRatioPlanned")
)
}
result$.plotSettings <- .self$.plotSettings
return(result)
},
show = function(showType = 1, digits = NA_integer_) {
"Method for automatically printing trial plan objects"
callSuper(showType = showType, digits = digits)
}
)
)
#'
#' @name TrialDesignPlanRates
#'
#' @title
#' Trial Design Plan Rates
#'
#' @description
#' Trial design plan for rates.
#'
#' @template field_riskRatio
#' @template field_thetaH0
#' @template field_normalApproximation
#' @template field_pi1
#' @template field_pi2
#' @template field_groups
#' @template field_allocationRatioPlanned
#' @template field_optimumAllocationRatio
#' @template field_directionUpper
#' @template field_effect
#' @template field_overallReject
#' @template field_rejectPerStage
#' @template field_futilityStop
#' @template field_futilityPerStage
#' @template field_earlyStop
#' @template field_expectedNumberOfSubjects
#' @template field_nFixed
#' @template field_nFixed1
#' @template field_nFixed2
#' @template field_informationRates
#' @template field_maxNumberOfSubjects
#' @template field_maxNumberOfSubjects1
#' @template field_maxNumberOfSubjects2
#' @template field_numberOfSubjects
#' @template field_numberOfSubjects1
#' @template field_numberOfSubjects2
#' @template field_expectedNumberOfSubjectsH0
#' @template field_expectedNumberOfSubjectsH01
#' @template field_expectedNumberOfSubjectsH1
#' @template field_criticalValuesEffectScale
#' @template field_criticalValuesEffectScaleLower
#' @template field_criticalValuesEffectScaleUpper
#' @template field_criticalValuesPValueScale
#' @template field_futilityBoundsEffectScale
#' @template field_futilityBoundsEffectScaleLower
#' @template field_futilityBoundsEffectScaleUpper
#' @template field_futilityBoundsPValueScale
#'
#' @details
#' This object cannot be created directly; use \code{\link[=getSampleSizeRates]{getSampleSizeRates()}}
#' with suitable arguments to create a design plan for a dataset of rates.
#'
#' @include class_core_parameter_set.R
#' @include class_design.R
#' @include class_design_set.R
#' @include f_core_constants.R
#'
#' @keywords internal
#'
#' @importFrom methods new
#'
TrialDesignPlanRates <- setRefClass("TrialDesignPlanRates",
contains = "TrialDesignPlan",
fields = list(
riskRatio = "logical",
thetaH0 = "numeric",
normalApproximation = "logical",
pi1 = "numeric",
pi2 = "numeric",
groups = "numeric",
allocationRatioPlanned = "numeric",
optimumAllocationRatio = "logical",
directionUpper = "logical",
effect = "numeric",
maxNumberOfSubjects = "numeric",
maxNumberOfSubjects1 = "numeric",
maxNumberOfSubjects2 = "numeric",
numberOfSubjects = "matrix",
numberOfSubjects1 = "matrix",
numberOfSubjects2 = "matrix",
overallReject = "numeric",
rejectPerStage = "matrix",
futilityStop = "numeric",
futilityPerStage = "matrix",
earlyStop = "numeric",
expectedNumberOfSubjects = "numeric",
nFixed = "numeric",
nFixed1 = "numeric",
nFixed2 = "numeric",
informationRates = "matrix",
expectedNumberOfSubjectsH0 = "numeric",
expectedNumberOfSubjectsH01 = "numeric",
expectedNumberOfSubjectsH1 = "numeric",
criticalValuesEffectScale = "matrix",
criticalValuesEffectScaleLower = "matrix",
criticalValuesEffectScaleUpper = "matrix",
criticalValuesPValueScale = "matrix",
futilityBoundsEffectScale = "matrix",
futilityBoundsEffectScaleLower = "matrix",
futilityBoundsEffectScaleUpper = "matrix",
futilityBoundsPValueScale = "matrix"
),
methods = list(
initialize = function(...,
normalApproximation = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_RATES[["normalApproximation"]],
riskRatio = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_RATES[["riskRatio"]],
thetaH0 = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_RATES[["thetaH0"]],
pi1 = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_RATES[["pi1"]],
pi2 = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_RATES[["pi2"]],
groups = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_RATES[["groups"]],
allocationRatioPlanned = C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_RATES[["allocationRatioPlanned"]]) {
callSuper(...,
normalApproximation = normalApproximation,
riskRatio = riskRatio,
thetaH0 = thetaH0,
pi1 = pi1,
pi2 = pi2,
groups = groups,
allocationRatioPlanned = allocationRatioPlanned
)
optimumAllocationRatio <<- FALSE
visibleFieldNames <- .getVisibleFieldNames()
startIndex <- which(visibleFieldNames == "directionUpper")
for (i in startIndex:length(visibleFieldNames)) {
.setParameterType(visibleFieldNames[i], C_PARAM_NOT_APPLICABLE)
}
if (groups == 1) {
.setParameterType("meanRatio", C_PARAM_NOT_APPLICABLE)
.setParameterType("allocationRatioPlanned", C_PARAM_NOT_APPLICABLE)
}
.setParameterType("maxNumberOfSubjects1", C_PARAM_NOT_APPLICABLE)
.setParameterType("maxNumberOfSubjects2", C_PARAM_NOT_APPLICABLE)
.setParameterType("criticalValuesEffectScale", C_PARAM_NOT_APPLICABLE)
.setParameterType("criticalValuesEffectScaleLower", C_PARAM_NOT_APPLICABLE)
.setParameterType("criticalValuesEffectScaleUpper", C_PARAM_NOT_APPLICABLE)
.setParameterType("futilityBoundsEffectScale", C_PARAM_NOT_APPLICABLE)
.setParameterType("futilityBoundsEffectScaleLower", C_PARAM_NOT_APPLICABLE)
.setParameterType("futilityBoundsEffectScaleUpper", C_PARAM_NOT_APPLICABLE)
},
clone = function(pi1 = NA_real_) {
pi1Temp <- pi1
if (any(is.na(pi1))) {
pi1Temp <- .self$pi1
}
if (.objectType == "sampleSize") {
return(getSampleSizeRates(
design = .self$.design,
normalApproximation = .self$.getParameterValueIfUserDefinedOrDefault("normalApproximation"),
riskRatio = .self$riskRatio, # .getParameterValueIfUserDefinedOrDefault("riskRatio"),
thetaH0 = .self$.getParameterValueIfUserDefinedOrDefault("thetaH0"),
pi1 = pi1Temp,
pi2 = .self$.getParameterValueIfUserDefinedOrDefault("pi2"),
groups = .self$.getParameterValueIfUserDefinedOrDefault("groups"),
allocationRatioPlanned = .self$.getParameterValueIfUserDefinedOrDefault("allocationRatioPlanned")
))
} else {
return(getPowerRates(
design = .self$.design,
riskRatio = .self$riskRatio, # .getParameterValueIfUserDefinedOrDefault("riskRatio"),
thetaH0 = .self$.getParameterValueIfUserDefinedOrDefault("thetaH0"),
pi1 = pi1Temp,
pi2 = .self$.getParameterValueIfUserDefinedOrDefault("pi2"),
directionUpper = .self$.getParameterValueIfUserDefinedOrDefault("directionUpper"),
maxNumberOfSubjects = .self$.getParameterValueIfUserDefinedOrDefault("maxNumberOfSubjects"),
groups = .self$.getParameterValueIfUserDefinedOrDefault("groups"),
allocationRatioPlanned = .self$.getParameterValueIfUserDefinedOrDefault("allocationRatioPlanned")
))
}
},
show = function(showType = 1, digits = NA_integer_) {
"Method for automatically printing trial plan objects"
callSuper(showType = showType, digits = digits)
}
)
)
#'
#' @name TrialDesignPlanSurvival
#'
#' @title
#' Trial Design Plan Survival
#'
#' @description
#' Trial design plan for survival data.
#'
#' @template field_thetaH0
#' @template field_typeOfComputation
#' @template field_directionUpper
#' @template field_pi1_survival
#' @template field_pi2_survival
#' @template field_median1
#' @template field_median2
#' @template field_lambda1
#' @template field_lambda2
#' @template field_hazardRatio
#' @template field_maxNumberOfSubjects
#' @template field_maxNumberOfSubjects1
#' @template field_maxNumberOfSubjects2
#' @template field_maxNumberOfEvents
#' @template field_allocationRatioPlanned
#' @template field_optimumAllocationRatio
#' @template field_accountForObservationTimes
#' @template field_eventTime
#' @template field_accrualTime
#' @template field_totalAccrualTime
#' @template field_accrualIntensity
#' @template field_accrualIntensityRelative
#' @template field_kappa
#' @template field_piecewiseSurvivalTime
#' @template field_followUpTime
#' @template field_dropoutRate1
#' @template field_dropoutRate2
#' @template field_dropoutTime
#' @template field_chi
#' @template field_expectedNumberOfEvents
#' @template field_eventsFixed
#' @template field_nFixed
#' @template field_nFixed1
#' @template field_nFixed2
#' @template field_overallReject
#' @template field_rejectPerStage
#' @template field_futilityStop
#' @template field_futilityPerStage
#' @template field_earlyStop
#' @template field_informationRates
#' @template field_analysisTime
#' @template field_studyDurationH1
#' @template field_studyDuration
#' @template field_maxStudyDuration
#' @template field_eventsPerStage
#' @template field_singleEventsPerStage
#' @template field_cumulativeEventsPerStage
#' @template field_expectedEventsH0
#' @template field_expectedEventsH01
#' @template field_expectedEventsH1
#' @template field_numberOfSubjects
#' @template field_numberOfSubjects1
#' @template field_numberOfSubjects2
#' @template field_expectedNumberOfSubjectsH1
#' @template field_expectedNumberOfSubjects
#' @template field_criticalValuesEffectScale
#' @template field_criticalValuesEffectScaleLower
#' @template field_criticalValuesEffectScaleUpper
#' @template field_criticalValuesPValueScale
#' @template field_futilityBoundsEffectScale
#' @template field_futilityBoundsEffectScaleLower
#' @template field_futilityBoundsEffectScaleUpper
#' @template field_futilityBoundsPValueScale
#'
#' @details
#' This object cannot be created directly; use \code{\link[=getSampleSizeSurvival]{getSampleSizeSurvival()}}
#' with suitable arguments to create a design plan for a dataset of survival data.
#'
#' @include class_core_parameter_set.R
#' @include class_design.R
#' @include class_design_set.R
#' @include class_time.R
#' @include f_core_constants.R
#'
#' @keywords internal
#'
#' @importFrom methods new
#'
TrialDesignPlanSurvival <- setRefClass("TrialDesignPlanSurvival",
contains = "TrialDesignPlan",
fields = list(
.piecewiseSurvivalTime = "PiecewiseSurvivalTime",
.accrualTime = "AccrualTime",
.calculateFollowUpTime = "logical",
thetaH0 = "numeric",
typeOfComputation = "character",
directionUpper = "logical",
pi1 = "numeric",
pi2 = "numeric",
median1 = "numeric",
median2 = "numeric",
lambda1 = "numeric",
lambda2 = "numeric",
hazardRatio = "numeric",
maxNumberOfSubjects = "numeric",
maxNumberOfSubjects1 = "numeric",
maxNumberOfSubjects2 = "numeric",
numberOfSubjects = "matrix",
numberOfSubjects1 = "matrix",
numberOfSubjects2 = "matrix",
maxNumberOfEvents = "numeric",
allocationRatioPlanned = "numeric",
optimumAllocationRatio = "logical",
accountForObservationTimes = "logical",
eventTime = "numeric",
accrualTime = "numeric",
totalAccrualTime = "numeric",
accrualIntensity = "numeric",
accrualIntensityRelative = "numeric",
kappa = "numeric",
piecewiseSurvivalTime = "numeric",
followUpTime = "numeric",
dropoutRate1 = "numeric",
dropoutRate2 = "numeric",
dropoutTime = "numeric",
chi = "numeric",
expectedNumberOfEvents = "numeric",
eventsFixed = "numeric",
nFixed = "numeric",
nFixed1 = "numeric",
nFixed2 = "numeric",
overallReject = "numeric",
rejectPerStage = "matrix",
futilityStop = "numeric",
futilityPerStage = "matrix",
earlyStop = "numeric",
informationRates = "matrix",
analysisTime = "matrix",
studyDurationH1 = "numeric",
studyDuration = "numeric",
maxStudyDuration = "numeric",
eventsPerStage = "matrix", # deprecated
singleEventsPerStage = "matrix",
cumulativeEventsPerStage = "matrix",
expectedEventsH0 = "numeric",
expectedEventsH01 = "numeric",
expectedEventsH1 = "numeric",
expectedNumberOfSubjectsH1 = "numeric",
expectedNumberOfSubjects = "numeric",
criticalValuesEffectScale = "matrix",
criticalValuesEffectScaleLower = "matrix",
criticalValuesEffectScaleUpper = "matrix",
criticalValuesPValueScale = "matrix",
futilityBoundsEffectScale = "matrix",
futilityBoundsEffectScaleLower = "matrix",
futilityBoundsEffectScaleUpper = "matrix",
futilityBoundsPValueScale = "matrix"
),
methods = list(
initialize = function(...) {
callSuper(...)
optimumAllocationRatio <<- FALSE
visibleFieldNames <- .getVisibleFieldNames()
startIndex <- which(visibleFieldNames == "hazardRatio")
for (i in startIndex:length(visibleFieldNames)) {
.setParameterType(visibleFieldNames[i], C_PARAM_NOT_APPLICABLE)
}
.setParameterType("maxNumberOfSubjects", C_PARAM_NOT_APPLICABLE)
.setParameterType("maxNumberOfSubjects1", C_PARAM_NOT_APPLICABLE)
.setParameterType("maxNumberOfSubjects2", C_PARAM_NOT_APPLICABLE)
.setParameterType("median1", C_PARAM_NOT_APPLICABLE)
.setParameterType("median2", C_PARAM_NOT_APPLICABLE)
.setParameterType("accountForObservationTimes", C_PARAM_NOT_APPLICABLE)
.setParameterType("chi", C_PARAM_NOT_APPLICABLE)
.setParameterType("maxStudyDuration", C_PARAM_NOT_APPLICABLE)
.setParameterType("accrualIntensityRelative", C_PARAM_NOT_APPLICABLE)
.setParameterType("criticalValuesEffectScale", C_PARAM_NOT_APPLICABLE)
.setParameterType("criticalValuesEffectScaleLower", C_PARAM_NOT_APPLICABLE)
.setParameterType("criticalValuesEffectScaleUpper", C_PARAM_NOT_APPLICABLE)
.setParameterType("futilityBoundsEffectScale", C_PARAM_NOT_APPLICABLE)
.setParameterType("futilityBoundsEffectScaleLower", C_PARAM_NOT_APPLICABLE)
.setParameterType("futilityBoundsEffectScaleUpper", C_PARAM_NOT_APPLICABLE)
.setParameterType("singleEventsPerStage", C_PARAM_NOT_APPLICABLE)
# set default values
for (parameterName in c(
"eventTime", "accrualTime", "accrualIntensity",
"kappa", "piecewiseSurvivalTime", "lambda1", "lambda2",
"followUpTime", "dropoutTime"
)) {
.setDefaultValue(parameterName)
}
},
clone = function(hazardRatio = NA_real_, pi1 = NA_real_) {
hr <- NA_real_
if (.getParameterType("hazardRatio") == C_PARAM_USER_DEFINED) {
hr <- hazardRatio
if (any(is.na(hazardRatio))) {
hr <- .self$hazardRatio
}
}
pi1Temp <- NA_real_
if (.getParameterType("pi1") == C_PARAM_USER_DEFINED) {
pi1Temp <- pi1
if (any(is.na(pi1))) {
pi1Temp <- .self$pi1
}
}
accrualTimeTemp <- .self$.getParameterValueIfUserDefinedOrDefault("accrualTime")
if (!is.null(accrualTimeTemp) && length(accrualTimeTemp) > 0 &&
!all(is.na(accrualTimeTemp)) && accrualTimeTemp[1] != 0) {
accrualTimeTemp <- c(0, accrualTimeTemp)
}
accrualIntensityTemp <- .self$.getParameterValueIfUserDefinedOrDefault("accrualIntensity")
if (all(is.na(accrualIntensityTemp))) {
accrualIntensityTemp <- C_ACCRUAL_INTENSITY_DEFAULT
}
if (.objectType == "sampleSize") {
return(getSampleSizeSurvival(
design = .self$.design,
typeOfComputation = .self$.getParameterValueIfUserDefinedOrDefault("typeOfComputation"),
thetaH0 = .self$.getParameterValueIfUserDefinedOrDefault("thetaH0"),
pi1 = pi1Temp,
pi2 = .self$.getParameterValueIfUserDefinedOrDefault("pi2"),
allocationRatioPlanned = .self$allocationRatioPlanned,
accountForObservationTimes = .self$.getParameterValueIfUserDefinedOrDefault("accountForObservationTimes"),
eventTime = .self$eventTime,
accrualTime = accrualTimeTemp,
accrualIntensity = accrualIntensityTemp,
kappa = .self$kappa,
piecewiseSurvivalTime = .self$.getParameterValueIfUserDefinedOrDefault("piecewiseSurvivalTime"),
lambda2 = .self$.getParameterValueIfUserDefinedOrDefault("lambda2"),
lambda1 = .self$.getParameterValueIfUserDefinedOrDefault("lambda1"),
followUpTime = .self$.getParameterValueIfUserDefinedOrDefault("followUpTime"),
maxNumberOfSubjects = .self$.getParameterValueIfUserDefinedOrDefault("maxNumberOfSubjects"),
dropoutRate1 = .self$dropoutRate1,
dropoutRate2 = .self$dropoutRate2,
dropoutTime = .self$dropoutTime,
hazardRatio = hr
))
} else {
directionUpperTemp <- directionUpper
if (length(directionUpperTemp) > 1) {
directionUpperTemp <- directionUpperTemp[1]
}
return(getPowerSurvival(
design = .self$.design,
typeOfComputation = .self$.getParameterValueIfUserDefinedOrDefault("typeOfComputation"),
thetaH0 = .self$.getParameterValueIfUserDefinedOrDefault("thetaH0"),
pi1 = pi1Temp,
pi2 = .self$.getParameterValueIfUserDefinedOrDefault("pi2"),
directionUpper = directionUpperTemp,
allocationRatioPlanned = .self$allocationRatioPlanned,
eventTime = .self$eventTime,
accrualTime = accrualTimeTemp,
accrualIntensity = accrualIntensityTemp,
kappa = .self$kappa,
piecewiseSurvivalTime = .self$.getParameterValueIfUserDefinedOrDefault("piecewiseSurvivalTime"),
lambda2 = .self$.getParameterValueIfUserDefinedOrDefault("lambda2"),
lambda1 = .self$.getParameterValueIfUserDefinedOrDefault("lambda1"),
hazardRatio = hr,
maxNumberOfSubjects = .self$.getParameterValueIfUserDefinedOrDefault("maxNumberOfSubjects"),
maxNumberOfEvents = .self$.getParameterValueIfUserDefinedOrDefault("maxNumberOfEvents"),
dropoutRate1 = .self$dropoutRate1,
dropoutRate2 = .self$dropoutRate2,
dropoutTime = .self$dropoutTime
))
}
},
.setDefaultValue = function(argumentName) {
if (is.null(.self[[argumentName]]) || all(is.na(.self[[argumentName]]))) {
.self[[argumentName]] <<- C_TRIAL_DESIGN_PLAN_DEFAULT_VALUES_SURVIVAL[[argumentName]]
.setParameterType(argumentName, C_PARAM_DEFAULT_VALUE)
}
},
show = function(showType = 1, digits = NA_integer_) {
"Method for automatically printing trial plan objects"
callSuper(showType = showType, digits = digits)
},
.warnInCaseArgumentExists = function(argument, argumentName) {
if (!all(is.na(argument)) && any(argument > 0)) {
warning(sprintf(
"Specified '%s' (%s) not taken into account",
argumentName, .arrayToString(argument)
), call. = FALSE)
}
}
)
)
#'
#' @name TrialDesignPlanCountData
#'
#' @title
#' Trial Design Plan Count Data
#'
#' @description
#' Trial design plan for count data.
#'
#' @template field_thetaH0
#' @template field_groups
#' @template field_allocationRatioPlanned
#' @template field_optimumAllocationRatio
#' @template field_directionUpper
#' @template field_lambda1
#' @template field_lambda2
#' @template field_lambda
#' @template field_theta
#' @template field_nFixed
#' @template field_nFixed1
#' @template field_nFixed2
#' @template field_maxNumberOfSubjects
#' @template field_maxNumberOfSubjects1
#' @template field_maxNumberOfSubjects2
#' @template field_overallReject
#' @template field_rejectPerStage
#' @template field_futilityStop
#' @template field_futilityPerStage
#' @template field_earlyStop
#' @template field_overdispersion
#' @template field_fixedExposureTime
#' @template field_accrualTime
#' @template field_accrualIntensity
#' @template field_followUpTime
#' @template field_calendarTime
#' @template field_expectedStudyDurationH1
#' @template field_studyTime
#' @template field_numberOfSubjects
#' @template field_expectedNumberOfSubjectsH1
#' @template field_informationOverStages
#' @template field_expectedInformationH0
#' @template field_expectedInformationH01
#' @template field_expectedInformationH1
#' @template field_maxInformation
#' @template field_futilityBoundsPValueScale
#'
#' @details
#' This object cannot be created directly; use \code{\link[=getSampleSizeCounts]{getSampleSizeCounts()}}
#' with suitable arguments to create a design plan for a dataset of rates.
#'
#' @include class_core_parameter_set.R
#' @include class_design.R
#' @include class_design_set.R
#' @include f_core_constants.R
#'
#' @keywords internal
#'
#' @importFrom methods new
#'
TrialDesignPlanCountData <- setRefClass("TrialDesignPlanCountData",
contains = "TrialDesignPlan",
fields = list(
.designCharacteristics = "ANY",
thetaH0 = "numeric",
groups = "integer",
allocationRatioPlanned = "numeric",
optimumAllocationRatio = "logical",
directionUpper = "logical",
lambda1 = "numeric",
lambda2 = "numeric",
lambda = "numeric",
theta = "numeric",
nFixed = "numeric",
nFixed1 = "numeric",
nFixed2 = "numeric",
maxNumberOfSubjects = "numeric",
maxNumberOfSubjects1 = "numeric",
maxNumberOfSubjects2 = "numeric",
overallReject = "numeric",
rejectPerStage = "matrix",
futilityStop = "numeric",
futilityPerStage = "matrix",
earlyStop = "numeric",
overdispersion = "numeric",
fixedExposureTime = "numeric",
accrualTime = "numeric",
accrualIntensity = "numeric",
followUpTime = "numeric",
calendarTime = "matrix",
expectedStudyDurationH1 = "numeric",
studyTime = "numeric",
numberOfSubjects = "matrix",
expectedNumberOfSubjectsH1 = "numeric",
informationOverStages = "matrix",
expectedInformationH0 = "numeric",
expectedInformationH01 = "numeric",
expectedInformationH1 = "numeric",
maxInformation = "numeric",
futilityBoundsPValueScale = "matrix"
),
methods = list(
initialize = function(...,
designCharacteristics,
lambda1 = NA_real_,
lambda2 = NA_real_,
lambda = NA_real_,
theta = NA_real_,
thetaH0 = 1,
overdispersion = 0,
fixedExposureTime = NA_real_,
accrualTime = NA_real_,
accrualIntensity = NA_real_,
followUpTime = NA_real_,
allocationRatioPlanned = NA_real_) {
callSuper(...,
.designCharacteristics = designCharacteristics,
lambda1 = lambda1,
lambda2 = lambda2,
lambda = lambda,
theta = theta,
thetaH0 = thetaH0,
overdispersion = overdispersion,
fixedExposureTime = fixedExposureTime,
accrualTime = accrualTime,
accrualIntensity = accrualIntensity,
followUpTime = followUpTime,
allocationRatioPlanned = allocationRatioPlanned
)
groups <<- 2L
optimumAllocationRatio <<- FALSE
.self$.setParameterType("groups", C_PARAM_NOT_APPLICABLE)
.self$.setParameterType("directionUpper", C_PARAM_NOT_APPLICABLE)
.self$.setParameterType("optimumAllocationRatio", C_PARAM_NOT_APPLICABLE)
},
.toString = function(startWithUpperCase = FALSE) {
s <- "count data"
return(ifelse(startWithUpperCase, .firstCharacterToUpperCase(s), s))
},
clone = function(..., lambda1 = NA_real_, theta = NA_real_) {
if (all(is.na(lambda1))) {
lambda1Temp <- .self$.getParameterValueIfUserDefinedOrDefault("lambda1")
} else {
lambda1Temp <- lambda1
if (any(is.na(lambda1))) {
lambda1Temp <- .self$lambda1
}
}
if (all(is.na(theta))) {
thetaTemp <- .self$.getParameterValueIfUserDefinedOrDefault("theta")
} else {
thetaTemp <- theta
if (any(is.na(theta))) {
thetaTemp <- .self$theta
}
}
if (.objectType == "sampleSize") {
result <- getSampleSizeCounts(
design = .self$.design,
lambda1 = lambda1Temp,
lambda2 = .self$.getParameterValueIfUserDefinedOrDefault("lambda2"),
lambda = .self$.getParameterValueIfUserDefinedOrDefault("lambda"),
theta = thetaTemp,
thetaH0 = .self$.getParameterValueIfUserDefinedOrDefault("thetaH0"),
overdispersion = .self$.getParameterValueIfUserDefinedOrDefault("overdispersion"),
fixedExposureTime = .self$.getParameterValueIfUserDefinedOrDefault("fixedExposureTime"),
accrualTime = .self$.getParameterValueIfUserDefinedOrDefault("accrualTime"),
accrualIntensity = .self$.getParameterValueIfUserDefinedOrDefault("accrualIntensity"),
followUpTime = .self$.getParameterValueIfUserDefinedOrDefault("followUpTime"),
maxNumberOfSubjects = .self$.getParameterValueIfUserDefinedOrDefault("maxNumberOfSubjects"),
allocationRatioPlanned = .self$.getParameterValueIfUserDefinedOrDefault("allocationRatioPlanned")
)
} else {
result <- getPowerCounts(
design = .self$.design,
directionUpper = .self$.getParameterValueIfUserDefinedOrDefault("directionUpper"),
maxNumberOfSubjects = .self$.getParameterValueIfUserDefinedOrDefault("maxNumberOfSubjects"),
lambda1 = lambda1Temp,
lambda2 = .self$.getParameterValueIfUserDefinedOrDefault("lambda2"),
lambda = .self$.getParameterValueIfUserDefinedOrDefault("lambda"),
theta = thetaTemp,
thetaH0 = .self$.getParameterValueIfUserDefinedOrDefault("thetaH0"),
overdispersion = .self$.getParameterValueIfUserDefinedOrDefault("overdispersion"),
fixedExposureTime = .self$.getParameterValueIfUserDefinedOrDefault("fixedExposureTime"),
accrualTime = .self$.getParameterValueIfUserDefinedOrDefault("accrualTime"),
accrualIntensity = .self$.getParameterValueIfUserDefinedOrDefault("accrualIntensity"),
followUpTime = .self$.getParameterValueIfUserDefinedOrDefault("followUpTime"),
allocationRatioPlanned = .self$.getParameterValueIfUserDefinedOrDefault("allocationRatioPlanned")
)
}
result$.plotSettings <- .self$.plotSettings
return(result)
}
)
)
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