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class_analysis_results.R
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class_analysis_results.R
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## |
## | *Analysis result 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: 7962 $
## | Last changed: $Date: 2024-05-31 13:41:37 +0200 (Fr, 31 Mai 2024) $
## | Last changed by: $Author: pahlke $
## |
#'
#' @name ConditionalPowerResults
#'
#' @title
#' Conditional Power Results
#'
#' @description
#' Class for conditional power calculations
#'
#' @template field_nPlanned
#' @template field_allocationRatioPlanned
#' @template field_iterations
#' @template field_seed
#' @template field_simulated
#' @template field_conditionalPower
#' @template field_thetaH1
#' @template field_assumedStDev
#'
#' @details
#' This object cannot be created directly; use \code{\link[=getConditionalPower]{getConditionalPower()}}
#' with suitable arguments to create the results of a group sequential or a combination test design.
#'
#' @keywords internal
#'
#' @importFrom methods new
#'
ConditionalPowerResults <- R6::R6Class("ConditionalPowerResults",
inherit = ParameterSet,
public = list(
.plotSettings = NULL,
.design = NULL,
.stageResults = NULL,
.plotData = NULL,
nPlanned = NULL,
allocationRatioPlanned = NULL,
iterations = NULL,
seed = NULL,
simulated = NULL,
initialize = function(..., .design = NULL, .stageResults = NULL, .plotData = NULL, nPlanned = NULL, allocationRatioPlanned = NULL, iterations = NULL, seed = NULL, simulated = NULL) {
self$.design <- .design
self$.stageResults <- .stageResults
self$.plotData <- .plotData
self$nPlanned <- nPlanned
self$allocationRatioPlanned <- allocationRatioPlanned
self$iterations <- iterations
self$seed <- seed
self$simulated <- simulated
super$initialize(...)
self$.plotSettings <- PlotSettings$new()
if (!is.null(self$.stageResults) && is.null(self$.design)) {
self$.design <- self$.stageResults$.design
}
if (is.null(self$simulated) || length(self$simulated) == 0 || is.na(self$simulated)) {
self$simulated <- FALSE
}
if (!is.null(self$.design) && length(self$.design$kMax) == 1 && self$.design$kMax == 1L) {
self$.setParameterType("nPlanned", C_PARAM_NOT_APPLICABLE)
self$.setParameterType("allocationRatioPlanned", C_PARAM_NOT_APPLICABLE)
self$.setParameterType("conditionalPower", C_PARAM_NOT_APPLICABLE)
} else {
self$.setParameterType("nPlanned", C_PARAM_GENERATED)
self$.setParameterType("allocationRatioPlanned", C_PARAM_USER_DEFINED)
self$.setParameterType("conditionalPower", C_PARAM_GENERATED)
}
self$.setParameterType("simulated", C_PARAM_NOT_APPLICABLE)
},
show = function(showType = 1, digits = NA_integer_) {
self$.show(showType = showType, digits = digits, consoleOutputEnabled = TRUE)
},
.show = function(showType = 1, digits = NA_integer_, consoleOutputEnabled = TRUE) {
"Method for automatically printing conditional power result objects"
self$.resetCat()
if (showType == 2) {
super$.show(showType = showType, digits = digits, consoleOutputEnabled = consoleOutputEnabled)
} else {
if (!is.null(self$.design) && length(self$.design$kMax) == 1 && self$.design$kMax == 1) {
self$.cat(self$.toString(), ": not applicable for fixed design (kMax = 1)\n",
heading = 1,
consoleOutputEnabled = consoleOutputEnabled
)
} else {
self$.cat(self$.toString(), ":\n\n",
heading = 1,
consoleOutputEnabled = consoleOutputEnabled
)
}
self$.showParametersOfOneGroup(self$.getUserDefinedParameters(), "User defined parameters",
orderByParameterName = FALSE, consoleOutputEnabled = consoleOutputEnabled
)
self$.showParametersOfOneGroup(self$.getDefaultParameters(), "Default parameters",
orderByParameterName = FALSE, consoleOutputEnabled = consoleOutputEnabled
)
self$.showParametersOfOneGroup(self$.getGeneratedParameters(), "Output",
orderByParameterName = FALSE, consoleOutputEnabled = consoleOutputEnabled
)
self$.showUnknownParameters(consoleOutputEnabled = consoleOutputEnabled)
}
},
.toString = function(startWithUpperCase = FALSE) {
return("Conditional power results")
}
)
)
#'
#' @name ConditionalPowerResultsMeans
#'
#' @title
#' Conditional Power Results Means
#'
#' @description
#' Class for conditional power calculations of means data
#'
#' @template field_nPlanned
#' @template field_allocationRatioPlanned
#' @template field_iterations
#' @template field_seed
#' @template field_simulated
#' @template field_conditionalPower
#' @template field_thetaH1
#' @template field_assumedStDev
#'
#' @details
#' This object cannot be created directly; use \code{\link{getConditionalPower}}
#' with suitable arguments to create the results of a group sequential or a combination test design.
#'
#' @keywords internal
#'
#' @importFrom methods new
#'
ConditionalPowerResultsMeans <- R6::R6Class("ConditionalPowerResultsMeans",
inherit = ConditionalPowerResults,
public = list(
conditionalPower = NULL,
thetaH1 = NULL,
assumedStDev = NULL,
initialize = function(..., conditionalPower = NULL, thetaH1 = NULL, assumedStDev = NULL) {
self$conditionalPower <- conditionalPower
self$thetaH1 <- thetaH1
self$assumedStDev <- assumedStDev
super$initialize(...)
if ((is.null(self$conditionalPower) || length(self$conditionalPower) == 0) &&
!is.null(self$.design) && !is.null(self$.design$kMax) && length(self$.design$kMax) > 0) {
self$conditionalPower <- rep(NA_real_, self$.design$kMax)
}
if (is.null(self$thetaH1) || length(self$thetaH1) == 0 || all(is.na(self$thetaH1))) {
self$thetaH1 <- NA_real_
}
if (is.null(self$assumedStDev) || length(self$assumedStDev) == 0 || all(is.na(self$assumedStDev))) {
self$assumedStDev <- NA_real_
}
},
.toString = function(startWithUpperCase = FALSE) {
return("Conditional power results means")
}
)
)
ConditionalPowerResultsMultiHypotheses <- R6::R6Class("ConditionalPowerResultsMultiHypotheses",
inherit = ConditionalPowerResults,
public = list(
conditionalPower = NULL,
initialize = function(..., conditionalPower = NULL) {
self$conditionalPower <- conditionalPower
super$initialize(...)
if (self$.readyForInitialization()) {
gMax <- self$getGMax()
kMax <- self$.design$kMax
if (is.null(self$conditionalPower) || (nrow(self$conditionalPower) == 0 && ncol(self$conditionalPower) == 0)) {
self$conditionalPower <- matrix(rep(NA_real_, gMax * kMax), nrow = gMax, ncol = kMax)
}
}
},
.toString = function(startWithUpperCase = FALSE) {
s <- "Conditional power results"
s <- paste0(s, " ", ifelse(grepl("Enrichment", .getClassName(self$.stageResults)), "enrichment", "multi-arm"))
if (grepl("Means", .getClassName(self))) {
s <- paste0(s, " means")
} else if (grepl("Rates", .getClassName(self))) {
s <- paste0(s, " rates")
} else if (grepl("Survival", .getClassName(self))) {
s <- paste0(s, " survival")
}
return(s)
},
getGMax = function() {
return(self$.stageResults$getGMax())
},
.readyForInitialization = function() {
if (is.null(self$.design)) {
return(FALSE)
}
if (length(self$.design$kMax) != 1) {
return(FALSE)
}
if (is.null(self$.stageResults)) {
return(FALSE)
}
if (is.null(self$.stageResults$testStatistics)) {
return(FALSE)
}
return(TRUE)
}
)
)
ConditionalPowerResultsMultiArmMeans <- R6::R6Class("ConditionalPowerResultsMultiArmMeans",
inherit = ConditionalPowerResultsMultiHypotheses,
public = list(
thetaH1 = NULL,
assumedStDevs = NULL,
initialize = function(..., thetaH1 = NULL, assumedStDevs = NULL) {
self$thetaH1 <- thetaH1
self$assumedStDevs <- assumedStDevs
super$initialize(...)
if (self$.readyForInitialization()) {
gMax <- self$getGMax()
if (is.null(self$thetaH1) || length(self$thetaH1) == 0 || all(is.na(self$thetaH1))) {
self$thetaH1 <- rep(NA_real_, gMax)
}
if (is.null(self$assumedStDevs) || length(self$assumedStDevs) == 0 || all(is.na(self$assumedStDevs))) {
self$assumedStDevs <- rep(NA_real_, gMax)
}
}
}
)
)
#'
#' @name ConditionalPowerResultsRates
#'
#' @title
#' Conditional Power Results Rates
#'
#' @description
#' Class for conditional power calculations of rates data
#'
#' @template field_nPlanned
#' @template field_allocationRatioPlanned
#' @template field_iterations
#' @template field_seed
#' @template field_simulated
#' @template field_conditionalPower
#' @template field_pi1
#' @template field_pi2
#'
#' @details
#' This object cannot be created directly; use \code{\link{getConditionalPower}}
#' with suitable arguments to create the results of a group sequential or a combination test design.
#'
#' @keywords internal
#'
#' @importFrom methods new
#'
ConditionalPowerResultsRates <- R6::R6Class("ConditionalPowerResultsRates",
inherit = ConditionalPowerResults,
public = list(
conditionalPower = NULL,
pi1 = NULL,
pi2 = NULL,
initialize = function(..., conditionalPower = NULL,
pi1 = NULL,
pi2 = NULL) {
self$conditionalPower <- conditionalPower
self$pi1 <- pi1
self$pi2 <- pi2
super$initialize(...)
if ((is.null(self$conditionalPower) || length(self$conditionalPower) == 0) &&
!is.null(self$.design) && !is.null(self$.design$kMax) && length(self$.design$kMax) > 0) {
self$conditionalPower <- rep(NA_real_, self$.design$kMax)
}
if (is.null(self$pi1) || length(self$pi1) == 0 || all(is.na(self$pi1))) {
self$pi1 <- NA_real_
}
if (is.null(self$pi2) || length(self$pi2) == 0 || all(is.na(self$pi2))) {
self$pi2 <- NA_real_
}
},
.toString = function(startWithUpperCase = FALSE) {
return("Conditional power results rates")
}
)
)
ConditionalPowerResultsMultiArmRates <- R6::R6Class("ConditionalPowerResultsMultiArmRates",
inherit = ConditionalPowerResultsMultiHypotheses,
public = list(
piTreatments = NULL,
piControl = NULL,
initialize = function(..., piTreatments = NULL, piControl = NULL) {
self$piTreatments <- piTreatments
self$piControl <- piControl
super$initialize(...)
if (self$.readyForInitialization()) {
gMax <- self$getGMax()
if (is.null(self$piControl) || length(self$piControl) == 0 || all(is.na(self$piControl))) {
self$piControl <- NA_real_
}
if (is.null(self$piTreatments) || length(self$piTreatments) == 0 || all(is.na(self$piTreatments))) {
self$piTreatments <- rep(NA_real_, gMax)
}
}
}
)
)
#'
#' @name ConditionalPowerResultsSurvival
#'
#' @title
#' Conditional Power Results Survival
#'
#' @description
#' Class for conditional power calculations of survival data
#'
#' @template field_nPlanned
#' @template field_allocationRatioPlanned
#' @template field_iterations
#' @template field_seed
#' @template field_simulated
#' @template field_conditionalPower
#' @template field_thetaH1_survival
#'
#' @details
#' This object cannot be created directly; use \code{\link{getConditionalPower}}
#' with suitable arguments to create the results of a group sequential or a combination test design.
#'
#' @keywords internal
#'
#' @importFrom methods new
#'
ConditionalPowerResultsSurvival <- R6::R6Class("ConditionalPowerResultsSurvival",
inherit = ConditionalPowerResults,
public = list(
conditionalPower = NULL,
thetaH1 = NULL,
initialize = function(..., conditionalPower = NULL, thetaH1 = NULL) {
self$conditionalPower <- conditionalPower
self$thetaH1 <- thetaH1
super$initialize(...)
if ((is.null(self$conditionalPower) || length(self$conditionalPower) == 0) &&
!is.null(self$.design) && !is.null(self$.design$kMax) && length(self$.design$kMax) > 0) {
self$conditionalPower <- rep(NA_real_, self$.design$kMax)
}
if (is.null(self$thetaH1) || length(self$thetaH1) == 0 || all(is.na(self$thetaH1))) {
self$thetaH1 <- NA_real_
}
},
.toString = function(startWithUpperCase = FALSE) {
return("Conditional power results survival")
}
)
)
ConditionalPowerResultsMultiArmSurvival <- R6::R6Class("ConditionalPowerResultsMultiArmSurvival",
inherit = ConditionalPowerResultsMultiHypotheses,
public = list(
thetaH1 = NULL,
initialize = function(..., thetaH1 = NULL) {
self$thetaH1 <- thetaH1
super$initialize(...)
if (self$.readyForInitialization()) {
gMax <- self$getGMax()
if (is.null(self$thetaH1) || length(self$thetaH1) == 0 || all(is.na(self$thetaH1))) {
self$thetaH1 <- rep(NA_real_, gMax)
}
}
}
)
)
#'
#' @name ConditionalPowerResultsEnrichmentMeans
#'
#' @title
#' Conditional Power Results Enrichment Means
#'
#' @description
#' Class for conditional power calculations of enrichment means data
#'
#' @template field_nPlanned
#' @template field_allocationRatioPlanned
#' @template field_iterations
#' @template field_seed
#' @template field_simulated
#' @template field_conditionalPower
#' @template field_thetaH1
#' @template field_assumedStDevs
#'
#' @details
#' This object cannot be created directly; use \code{\link{getConditionalPower}}
#' with suitable arguments to create the results of a group sequential or a combination test design.
#'
#' @keywords internal
#'
#' @importFrom methods new
#'
ConditionalPowerResultsEnrichmentMeans <- R6::R6Class("ConditionalPowerResultsEnrichmentMeans",
inherit = ConditionalPowerResultsMultiArmMeans
)
#'
#' @name ConditionalPowerResultsEnrichmentRates
#'
#' @title
#' Conditional Power Results Enrichment Rates
#'
#' @description
#' Class for conditional power calculations of enrichment rates data
#'
#' @template field_nPlanned
#' @template field_allocationRatioPlanned
#' @template field_iterations
#' @template field_seed
#' @template field_simulated
#' @template field_conditionalPower
#' @template field_piTreatments
#' @template field_piControls
#'
#' @details
#' This object cannot be created directly; use \code{\link{getConditionalPower}}
#' with suitable arguments to create the results of a group sequential or a combination test design.
#'
#' @keywords internal
#'
#' @importFrom methods new
#'
ConditionalPowerResultsEnrichmentRates <- R6::R6Class("ConditionalPowerResultsEnrichmentRates",
inherit = ConditionalPowerResultsMultiHypotheses,
public = list(
piTreatments = NULL,
piControls = NULL,
initialize = function(..., piTreatments = NULL, piControls = NULL) {
self$piTreatments <- piTreatments
self$piControls <- piControls
super$initialize(...)
if (self$.readyForInitialization()) {
gMax <- self$getGMax()
if (is.null(self$piControls) || length(self$piControls) == 0 || all(is.na(self$piControls))) {
self$piControls <- rep(NA_real_, gMax)
}
if (is.null(self$piTreatments) || length(self$piTreatments) == 0 || all(is.na(self$piTreatments))) {
self$piTreatments <- rep(NA_real_, gMax)
}
}
}
)
)
ConditionalPowerResultsEnrichmentSurvival <- R6::R6Class("ConditionalPowerResultsEnrichmentSurvival",
inherit = ConditionalPowerResultsMultiArmSurvival
)
#'
#' @name ClosedCombinationTestResults
#'
#' @title
#' Analysis Results Closed Combination Test
#'
#' @description
#' Class for multi-arm analysis results based on a closed combination test.
#'
#' @template field_intersectionTest
#' @template field_indices
#' @template field_adjustedStageWisePValues
#' @template field_overallAdjustedTestStatistics
#' @template field_separatePValues
#' @template field_conditionalErrorRate
#' @template field_secondStagePValues
#' @template field_rejected
#' @template field_rejectedIntersections
#'
#' @details
#' This object cannot be created directly; use \code{\link{getAnalysisResults}}
#' with suitable arguments to create the multi-arm analysis results of a closed combination test design.
#'
#' @keywords internal
#'
#' @importFrom methods new
#'
ClosedCombinationTestResults <- R6::R6Class("ClosedCombinationTestResults",
inherit = ParameterSet,
public = list(
.plotSettings = NULL,
.design = NULL,
.enrichment = NULL,
intersectionTest = NULL,
indices = NULL,
adjustedStageWisePValues = NULL,
overallAdjustedTestStatistics = NULL,
separatePValues = NULL,
conditionalErrorRate = NULL,
secondStagePValues = NULL,
rejected = NULL,
rejectedIntersections = NULL,
initialize = function(..., .design = NULL,
.enrichment = NULL,
intersectionTest = NULL,
indices = NULL,
adjustedStageWisePValues = NULL,
overallAdjustedTestStatistics = NULL,
separatePValues = NULL,
conditionalErrorRate = NULL,
secondStagePValues = NULL,
rejected = NULL,
rejectedIntersections = NULL) {
self$.design <- .design
self$.enrichment <- .enrichment
self$intersectionTest <- intersectionTest
self$indices <- indices
self$adjustedStageWisePValues <- adjustedStageWisePValues
self$overallAdjustedTestStatistics <- overallAdjustedTestStatistics
self$separatePValues <- separatePValues
self$conditionalErrorRate <- conditionalErrorRate
self$secondStagePValues <- secondStagePValues
self$rejected <- rejected
self$rejectedIntersections <- rejectedIntersections
super$initialize(...)
self$.plotSettings <- PlotSettings$new()
self$.setParameterType("intersectionTest", C_PARAM_USER_DEFINED)
parametersGenerated <- c(
"indices",
"separatePValues",
"rejected",
"rejectedIntersections"
)
if (inherits(self$.design, "TrialDesignConditionalDunnett")) {
parametersGenerated <- c(
parametersGenerated,
"conditionalErrorRate",
"secondStagePValues"
)
} else {
parametersGenerated <- c(
parametersGenerated,
"adjustedStageWisePValues",
"overallAdjustedTestStatistics"
)
}
for (param in parametersGenerated) {
self$.setParameterType(param, C_PARAM_GENERATED)
}
},
show = function(showType = 1, digits = NA_integer_) {
self$.show(showType = showType, digits = digits, consoleOutputEnabled = TRUE)
},
.show = function(showType = 1, digits = NA_integer_, consoleOutputEnabled = TRUE) {
"Method for automatically printing closed combination test result objects"
self$.resetCat()
if (showType == 2) {
super$.show(showType = showType, digits = digits, consoleOutputEnabled = consoleOutputEnabled)
} else {
self$.cat(self$.toString(), ":\n\n",
heading = 1,
consoleOutputEnabled = consoleOutputEnabled
)
self$.showParametersOfOneGroup(self$.getUserDefinedParameters(), "User defined parameters",
orderByParameterName = FALSE, consoleOutputEnabled = consoleOutputEnabled
)
designParametersToShow <- c(
".design$stages",
".design$alpha"
)
if (inherits(self$.design, "TrialDesignConditionalDunnett")) {
designParametersToShow <- c(
designParametersToShow,
".design$informationAtInterim",
".design$secondStageConditioning"
)
}
self$.showParametersOfOneGroup(designParametersToShow, "Design parameters",
orderByParameterName = FALSE, consoleOutputEnabled = consoleOutputEnabled
)
self$.showParametersOfOneGroup(self$.getGeneratedParameters(), "Output",
orderByParameterName = FALSE, consoleOutputEnabled = consoleOutputEnabled
)
self$.showUnknownParameters(consoleOutputEnabled = consoleOutputEnabled)
self$.cat("Legend:\n", heading = 2, consoleOutputEnabled = consoleOutputEnabled)
if (isTRUE(self$.enrichment)) {
self$.cat(paste0(" S[i]: population i\n"), consoleOutputEnabled = consoleOutputEnabled)
self$.cat(paste0(" F: full population\n"), consoleOutputEnabled = consoleOutputEnabled)
} else {
self$.cat(paste0(
" (i): results of treatment arm i vs. control group ",
(nrow(self$separatePValues) + 1), "\n"
), consoleOutputEnabled = consoleOutputEnabled)
self$.cat(" [i]: hypothesis number\n",
consoleOutputEnabled = consoleOutputEnabled
)
}
}
},
.toString = function(startWithUpperCase = FALSE) {
s <- "Closed combination test results"
if (inherits(self$.design, "TrialDesignConditionalDunnett")) {
s <- paste0(s, " (Conditional Dunnett)")
}
return(s)
},
.getHypothesisTreatmentArms = function(number) {
result <- c()
for (i in 1:ncol(self$indices)) {
if (self$indices[number, i] == 1) {
result <- c(result, i)
}
}
return(result)
},
.getHypothesisTreatmentArmVariants = function() {
result <- c()
for (number in 1:nrow(self$indices)) {
arms <- self$.getHypothesisTreatmentArms(number)
result <- c(result, paste0(arms, collapse = ", "))
}
return(result)
},
.getHypothesisPopulationVariants = function() {
result <- c()
gMax <- 1
for (number in 1:nrow(self$indices)) {
arms <- self$.getHypothesisTreatmentArms(number)
if (number == 1) {
gMax <- length(arms)
}
arms <- paste0("S", arms)
arms[arms == paste0("S", gMax)] <- "F"
result <- c(result, paste0(arms, collapse = ", "))
}
return(result)
}
)
)
#'
#' @name AnalysisResults
#'
#' @title
#' Basic Class for Analysis Results
#'
#' @description
#' A basic class for analysis results.
#'
#' @details
#' \code{AnalysisResults} is the basic class for
#' \itemize{
#' \item \code{\link{AnalysisResultsFisher}},
#' \item \code{\link{AnalysisResultsGroupSequential}},
#' \item \code{\link{AnalysisResultsInverseNormal}},
#' \item \code{\link{AnalysisResultsMultiArmFisher}},
#' \item \code{\link{AnalysisResultsMultiArmInverseNormal}},
#' \item \code{\link{AnalysisResultsConditionalDunnett}},
#' \item \code{\link{AnalysisResultsEnrichmentFisher}},
#' \item \code{\link{AnalysisResultsEnrichmentInverseNormal}}.
#' }
#'
#' @include class_core_parameter_set.R
#' @include class_core_plot_settings.R
#' @include class_analysis_dataset.R
#' @include class_design.R
#' @include f_core_constants.R
#' @include class_analysis_stage_results.R
#'
#' @keywords internal
#'
#' @importFrom methods new
#'
AnalysisResults <- R6::R6Class("AnalysisResults",
inherit = ParameterSet,
public = list(
.plotSettings = NULL,
.design = NULL,
.dataInput = NULL,
.stageResults = NULL,
.conditionalPowerResults = NULL,
normalApproximation = NULL,
directionUpper = NULL,
thetaH0 = NULL,
pi1 = NULL,
pi2 = NULL,
nPlanned = NULL,
allocationRatioPlanned = NULL,
initialize = function(design, dataInput, ..., .stageResults = NULL, .conditionalPowerResults = NULL, directionUpper = NULL, thetaH0 = NULL) {
self$.design <- design
self$.dataInput <- dataInput
self$.stageResults <- .stageResults
self$.conditionalPowerResults <- .conditionalPowerResults
self$directionUpper <- directionUpper
self$thetaH0 <- thetaH0
super$initialize(...)
self$.plotSettings <- PlotSettings$new()
},
.setStageResults = function(stageResults) {
self$.stageResults <- stageResults
},
getPlotSettings = function() {
return(self$.plotSettings)
},
show = function(showType = 1, digits = NA_integer_) {
self$.show(showType = showType, digits = digits, consoleOutputEnabled = TRUE)
},
.getStageResultParametersToShow = function() {
stageResultParametersToShow <- c()
if (self$.design$kMax > 1) {
if (!grepl("Rates", .getClassName(self$.dataInput)) || self$.dataInput$getNumberOfGroups() > 1) {
stageResultParametersToShow <- c(stageResultParametersToShow, ".stageResults$effectSizes")
}
if (grepl("Means", .getClassName(self$.dataInput))) {
stageResultParametersToShow <- c(stageResultParametersToShow, ".stageResults$overallStDevs")
}
if (grepl("Rates", .getClassName(self$.dataInput))) {
if (.isMultiArmAnalysisResults(self)) {
stageResultParametersToShow <- c(stageResultParametersToShow, ".stageResults$overallPiTreatments")
stageResultParametersToShow <- c(stageResultParametersToShow, ".stageResults$overallPiControl")
} else if (.isEnrichmentAnalysisResults(self)) {
stageResultParametersToShow <- c(stageResultParametersToShow, ".stageResults$overallPisTreatment")
stageResultParametersToShow <- c(stageResultParametersToShow, ".stageResults$overallPisControl")
} else {
stageResultParametersToShow <- c(stageResultParametersToShow, ".stageResults$overallPi1")
if (self$.dataInput$getNumberOfGroups() > 1) {
stageResultParametersToShow <- c(stageResultParametersToShow, ".stageResults$overallPi2")
}
}
}
}
stageResultParametersToShow <- c(stageResultParametersToShow, ".stageResults$testStatistics")
if (grepl("(MultiArm|Dunnett|Enrichment)", .getClassName(self))) {
stageResultParametersToShow <- c(stageResultParametersToShow, ".stageResults$separatePValues")
} else {
stageResultParametersToShow <- c(stageResultParametersToShow, ".stageResults$pValues")
}
if (self$.design$kMax == 1) {
# return(stageResultParametersToShow)
}
# show combination test statistics
if (.isTrialDesignInverseNormal(self$.design)) {
stageResultParametersToShow <- c(stageResultParametersToShow, ".stageResults$combInverseNormal")
} else if (.isTrialDesignGroupSequential(self$.design)) {
stageResultParametersToShow <- c(stageResultParametersToShow, ".stageResults$overallTestStatistics")
stageResultParametersToShow <- c(stageResultParametersToShow, ".stageResults$overallPValues")
} else if (.isTrialDesignFisher(self$.design)) {
stageResultParametersToShow <- c(stageResultParametersToShow, ".stageResults$combFisher")
}
return(stageResultParametersToShow)
},
.show = function(showType = 1, digits = NA_integer_, consoleOutputEnabled = TRUE) {
"Method for automatically printing analysis result objects"
self$.resetCat()
if (showType == 2) {
super$.show(showType = showType, digits = digits, consoleOutputEnabled = consoleOutputEnabled)
} else {
self$.cat(self$.toString(startWithUpperCase = TRUE), ":\n\n",
heading = 1,
consoleOutputEnabled = consoleOutputEnabled
)
self$.showParametersOfOneGroup(.getDesignParametersToShow(self), "Design parameters",
orderByParameterName = FALSE, consoleOutputEnabled = consoleOutputEnabled
)
self$.showParametersOfOneGroup(self$.getUserDefinedParameters(), "User defined parameters",
orderByParameterName = FALSE, consoleOutputEnabled = consoleOutputEnabled
)
self$.showParametersOfOneGroup(self$.getDefaultParameters(), "Default parameters",
orderByParameterName = FALSE, consoleOutputEnabled = consoleOutputEnabled
)
self$.showParametersOfOneGroup(self$.getStageResultParametersToShow(), "Stage results",
orderByParameterName = FALSE, consoleOutputEnabled = consoleOutputEnabled
)
# show multi-arm parameters
if (grepl("(MultiArm|Dunnett|Enrichment)", .getClassName(self))) {
if (.isTrialDesignConditionalDunnett(self$.design)) {
self$.showParametersOfOneGroup(".closedTestResults$conditionalErrorRate",
"Conditional error rate",
orderByParameterName = FALSE,
consoleOutputEnabled = consoleOutputEnabled
)
self$.showParametersOfOneGroup(".closedTestResults$secondStagePValues",
"Second stage p-values",
orderByParameterName = FALSE,
consoleOutputEnabled = consoleOutputEnabled
)
} else {
self$.showParametersOfOneGroup(".closedTestResults$adjustedStageWisePValues",
"Adjusted stage-wise p-values",
orderByParameterName = FALSE,
consoleOutputEnabled = consoleOutputEnabled
)
self$.showParametersOfOneGroup(".closedTestResults$overallAdjustedTestStatistics",
"Overall adjusted test statistics",
orderByParameterName = FALSE,
consoleOutputEnabled = consoleOutputEnabled
)
}
self$.showParametersOfOneGroup(".closedTestResults$rejected", "Test actions",
orderByParameterName = FALSE, consoleOutputEnabled = consoleOutputEnabled
)
}
generatedParams <- self$.getGeneratedParameters()
generatedParams <- generatedParams[!(generatedParams %in%
c("assumedStDevs", "thetaH1", "pi1", "pi2", "piTreatments", "piTreatments", "piControl", "piControls"))]
if (grepl("(MultiArm|Dunnett|Enrichment)", .getClassName(self))) {
if (all(c("conditionalPowerSimulated", "conditionalRejectionProbabilities") %in% generatedParams)) {
generatedParams <- .moveValue(
generatedParams,
"conditionalPowerSimulated", "conditionalRejectionProbabilities"
)
}
self$.showParametersOfOneGroup(generatedParams, "Further analysis results",
orderByParameterName = FALSE, consoleOutputEnabled = consoleOutputEnabled
)
} else {
self$.showParametersOfOneGroup(generatedParams, "Analysis results",
orderByParameterName = FALSE, consoleOutputEnabled = consoleOutputEnabled
)
}
self$.showUnknownParameters(consoleOutputEnabled = consoleOutputEnabled)
if (grepl("(MultiArm|Dunnett)", .getClassName(self))) {
self$.cat("Legend:\n", heading = 2, consoleOutputEnabled = consoleOutputEnabled)
self$.cat(
paste0(
" (i): results of treatment arm i vs. control group ",
self$.dataInput$getNumberOfGroups(), "\n"
),
consoleOutputEnabled = consoleOutputEnabled
)
} else if (.isEnrichmentAnalysisResults(self)) {
self$.cat("Legend:\n", heading = 2, consoleOutputEnabled = consoleOutputEnabled)
self$.cat(paste0(" S[i]: population i\n"), consoleOutputEnabled = consoleOutputEnabled)
self$.cat(paste0(" F: full population\n"), consoleOutputEnabled = consoleOutputEnabled)
} else if (grepl("Rates", .getClassName(self$.dataInput)) && self$.dataInput$getNumberOfGroups() == 2) {
self$.cat("Legend:\n", heading = 2, consoleOutputEnabled = consoleOutputEnabled)
self$.cat(" (i): values of treatment arm i\n", consoleOutputEnabled = consoleOutputEnabled)
}
}
},
.toString = function(startWithUpperCase = FALSE) {
str <- "analysis results"
if (inherits(self, "AnalysisResultsMultiArm")) {
str <- paste0("multi-arm ", str)
} else if (inherits(self, "AnalysisResultsEnrichment")) {
str <- paste0("enrichment ", str)
}
if (startWithUpperCase) {
str <- .firstCharacterToUpperCase(str)
}
numberOfGroups <- self$.dataInput$getNumberOfGroups()
str <- paste0(str, " (")
str <- paste0(str, tolower(sub("Dataset(Enrichment)?", "", .getClassName(self$.dataInput))))
if (grepl("Survival", .getClassName(.getClassName))) {
str <- paste0(str, " data")
}
if (numberOfGroups == 1) {
str <- paste0(str, " of one group")
} else {
str <- paste0(str, " of ", numberOfGroups, " groups")
}
if (self$.design$kMax > 1) {
if (grepl("GroupSequential", .getClassName(self))) {
str <- paste0(str, ", group sequential design")
} else if (grepl("InverseNormal", .getClassName(self))) {
str <- paste0(str, ", inverse normal combination test design")
} else if (grepl("Fisher", .getClassName(self))) {
str <- paste0(str, ", Fisher's combination test design")
} else if (grepl("Dunnett", .getClassName(self))) {
str <- paste0(str, ", conditional Dunnett design")
}
} else {
str <- paste0(str, ", fixed sample size design")
}
str <- paste0(str, ")")
return(str)
},
getNumberOfStages = function() {
return(self$.stageResults$getNumberOfStages())
},
getDataInput = function() {
return(self$.dataInput)
}
)
)
AnalysisResultsBase <- R6::R6Class("AnalysisResultsBase",
inherit = AnalysisResults,
public = list(
thetaH1 = NULL,
assumedStDev = NULL,
equalVariances = NULL,
testActions = NULL,
conditionalRejectionProbabilities = NULL,
conditionalPower = NULL,
repeatedConfidenceIntervalLowerBounds = NULL,
repeatedConfidenceIntervalUpperBounds = NULL,
repeatedPValues = NULL,
finalStage = NULL,
finalPValues = NULL,
finalConfidenceIntervalLowerBounds = NULL,
finalConfidenceIntervalUpperBounds = NULL,
medianUnbiasedEstimates = NULL,
initialize = function(design, dataInput, ..., thetaH1 = NULL,
assumedStDev = NULL,
equalVariances = NULL,
testActions = NULL,
conditionalRejectionProbabilities = NULL,
conditionalPower = NULL,
repeatedConfidenceIntervalLowerBounds = NULL,
repeatedConfidenceIntervalUpperBounds = NULL,
repeatedPValues = NULL,
finalStage = NULL,
finalPValues = NULL,
finalConfidenceIntervalLowerBounds = NULL,
finalConfidenceIntervalUpperBounds = NULL,
medianUnbiasedEstimates = NULL) {
self$thetaH1 <- thetaH1
self$assumedStDev <- assumedStDev
self$equalVariances <- equalVariances
self$testActions <- testActions
self$conditionalRejectionProbabilities <- conditionalRejectionProbabilities
self$conditionalPower <- conditionalPower
self$repeatedConfidenceIntervalLowerBounds <- repeatedConfidenceIntervalLowerBounds
self$repeatedConfidenceIntervalUpperBounds <- repeatedConfidenceIntervalUpperBounds
self$repeatedPValues <- repeatedPValues
self$finalStage <- finalStage
self$finalPValues <- finalPValues
self$finalConfidenceIntervalLowerBounds <- finalConfidenceIntervalLowerBounds
self$finalConfidenceIntervalUpperBounds <- finalConfidenceIntervalUpperBounds
self$medianUnbiasedEstimates <- medianUnbiasedEstimates
super$initialize(design = design, dataInput = dataInput, ...)
self$finalStage <- NA_integer_