Merge pull request #13 from rpact-com/dev/3.5.0

New CRAN release version 3.5.0

.Rbuildignore

@@ -39,3 +39,11 @@ lastMiKTeXException
 ^_pkgdown\.yml$
 ^pkgdown$
 ^rpact-tests$
+^tests-complete$
+testthat-problems\.rds
+^\.covrignore$
+^tests-complete$
+^.*\.gcda$
+^.*\.gcno$
+^inst/\.covrignore$
+^README\.html$

.covrignore

@@ -0,0 +1,9 @@
+R/pkgname.R
+R/class_core_plot_settings.R
+R/f_quality_assurance.R
+R/f_core_plot.R
+R/class_design_plan.R
+R/class_event_probabilities.R
+R/class_simulation_results.R
+R/f_assertions.cpp
+src/f_utilities.h

.gitignore

@@ -23,3 +23,9 @@ lastMiKTeXException
 /.project
 docs
 rpact-tests
+tests-complete
+testthat-problems.rds
+/tests/testthat/_examples
+/src/*.gcno
+/src/*.gcda
+/README.html

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: rpact
 Title: Confirmatory Adaptive Clinical Trial Design and Analysis
-Version: 3.4.0
-Date: 2023-07-03
+Version: 3.5.0
+Date: 2024-01-25
 Authors@R: c(
 	person(
 		given = "Gernot", 
@@ -28,6 +28,13 @@ Authors@R: c(
     	family = "Schueuerhuis", 
     	email = "[email protected]",
     	role = c("ctb")
+	),
+    person(
+    	given = "Tobias", 
+    	family = "Muetze", 
+    	email = "[email protected]",
+    	comment = c(ORCID = "0000-0002-4111-1941"), 
+    	role = c("ctb")
 	))
 Description: Design and analysis of confirmatory adaptive clinical trials with continuous, binary, and survival endpoints according to the methods described in the monograph by Wassmer and Brannath (2016) <doi:10.1007/978-3-319-32562-0>. This includes classical group sequential as well as multi-stage adaptive hypotheses tests that are based on the combination testing principle.
 License: LGPL-3
@@ -54,10 +61,9 @@ LinkingTo: Rcpp
 Suggests: 
 	ggplot2 (>= 2.2.0),	
 	testthat (>= 3.0.0),
-	mnormt (>= 1.5-7),
 	rmarkdown (>= 1.10)
 VignetteBuilder: knitr, rmarkdown
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
 Roxygen: list(markdown = TRUE)
 Config/testthat/edition: 3
 Config/testthat/parallel: true
@@ -79,9 +85,9 @@ Collate:
     'class_analysis_dataset.R'
     'class_analysis_stage_results.R'
     'class_analysis_results.R'
+    'f_design_general_utilities.R'
     'class_time.R'
     'class_design_set.R'
-    'f_design_utilities.R'
     'class_design_plan.R'
     'class_design_power_and_asn.R'
     'class_event_probabilities.R'
@@ -102,10 +108,16 @@ Collate:
     'f_analysis_multiarm_means.R'
     'f_analysis_multiarm_rates.R'
     'f_analysis_multiarm_survival.R'
+    'f_as251.R'
     'f_core_output_formats.R'
     'f_design_fisher_combination_test.R'
     'f_design_group_sequential.R'
-    'f_design_sample_size_calculator.R'
+    'f_design_plan_count_data.R'
+    'f_design_plan_means.R'
+    'f_design_plan_plot.R'
+    'f_design_plan_rates.R'
+    'f_design_plan_survival.R'
+    'f_design_plan_utilities.R'
     'f_quality_assurance.R'
     'f_simulation_base_means.R'
     'f_simulation_base_rates.R'
@@ -119,5 +131,6 @@ Collate:
     'f_simulation_multiarm_rates.R'
     'f_simulation_multiarm_survival.R'
     'f_simulation_performance_score.R'
+    'f_simulation_plot.R'
     'parameter_descriptions.R'
     'pkgname.R'

NAMESPACE

@@ -10,6 +10,7 @@ S3method(as.data.frame,TrialDesignPlan)
 S3method(as.data.frame,TrialDesignSet)
 S3method(as.matrix,FieldSet)
 S3method(knit_print,ParameterSet)
+S3method(knit_print,SummaryFactory)
 S3method(length,TrialDesignSet)
 S3method(names,AnalysisResults)
 S3method(names,FieldSet)
@@ -38,6 +39,8 @@ S3method(summary,AnalysisResults)
 S3method(summary,Dataset)
 S3method(summary,ParameterSet)
 S3method(summary,TrialDesignSet)
+export(as251Normal)
+export(as251StudentT)
 export(getAccrualTime)
 export(getAnalysisResults)
 export(getAvailablePlotTypes)
@@ -82,12 +85,14 @@ export(getPiecewiseExponentialRandomNumbers)
 export(getPiecewiseSurvivalTime)
 export(getPlotSettings)
 export(getPowerAndAverageSampleNumber)
+export(getPowerCounts)
 export(getPowerMeans)
 export(getPowerRates)
 export(getPowerSurvival)
 export(getRawData)
 export(getRepeatedConfidenceIntervals)
 export(getRepeatedPValues)
+export(getSampleSizeCounts)
 export(getSampleSizeMeans)
 export(getSampleSizeRates)
 export(getSampleSizeSurvival)
@@ -105,6 +110,8 @@ export(getTestActions)
 export(getWideFormat)
 export(kable)
 export(kable.ParameterSet)
+export(mvnprd)
+export(mvstud)
 export(plotTypes)
 export(ppwexp)
 export(printCitation)
@@ -132,4 +139,4 @@ importFrom(knitr,kable)
 importFrom(knitr,knit_print)
 importFrom(methods,new)
 importFrom(rlang,.data)
-useDynLib(rpact, .registration = TRUE)
+useDynLib(rpact,.registration = TRUE)

NEWS.md

@@ -1,4 +1,20 @@
 
+# rpact 3.5.0
+
+## New features
+
+* The new functions `getSampleSizeCounts()` and `getPowerCounts()` can be used to perform sample size calculations and the assessment of test characteristics for clinical trials with negative binomial distributed count data. This is possible for fixed sample size and group sequential designs. For the latter, the methodology described in Muetze et al. (2019) is implemented. These functions can also be used to perform blinded sample size reassessments according to Friede and Schmidli (2010).
+
+## Improvements, issues, and changes
+
+* Original Fortran 77 code of AS 251 included into the package, see functions `mvnprd`, `mvstud`, `as251Normal`, and `as251StudentT`
+* R package `mnormt` dependency has been removed 
+* Argument `theta` can be used for plotting of sample size and power results
+* Pipe operator usage improved
+* Shiny app link changed to https://rpact.shinyapps.io/cloud
+* Several minor improvements
+
+
 # rpact 3.4.0
 
 ## New features

R/RcppExports.R

@@ -1,67 +1,79 @@
 # Generated by using Rcpp::compileAttributes() -> do not edit by hand
 # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
-getFisherCombinationSizeCpp <- function(kMax, alpha0Vec, criticalValues, tVec, cases) {
+.mvnprd <- function(a, b, bpd, eps, inf, ierc, hinc) {
+    .Call(`_rpact_mvnprd`, a, b, bpd, eps, inf, ierc, hinc)
+}
+
+.mvstud <- function(ndf, a, b, bpd, d, eps, inf, ierc, hnc) {
+    .Call(`_rpact_mvstud`, ndf, a, b, bpd, d, eps, inf, ierc, hnc)
+}
+
+.getNegativeBinomialEstimates <- function(counts1, counts2, t1, t2) {
+    .Call(`_rpact_estimate_nb`, counts1, counts2, t1, t2)
+}
+
+.getFisherCombinationSizeCpp <- function(kMax, alpha0Vec, criticalValues, tVec, cases) {
     .Call(`_rpact_getFisherCombinationSizeCpp`, kMax, alpha0Vec, criticalValues, tVec, cases)
 }
 
-getSimulatedAlphaCpp <- function(kMax, alpha0, criticalValues, tVec, iterations) {
+.getSimulatedAlphaCpp <- function(kMax, alpha0, criticalValues, tVec, iterations) {
     .Call(`_rpact_getSimulatedAlphaCpp`, kMax, alpha0, criticalValues, tVec, iterations)
 }
 
-getFisherCombinationCasesCpp <- function(kMax, tVec) {
+.getFisherCombinationCases <- function(kMax, tVec) {
     .Call(`_rpact_getFisherCombinationCasesCpp`, kMax, tVec)
 }
 
-getDesignFisherTryCpp <- function(kMax, alpha, tolerance, criticalValues, scale, alpha0Vec, userAlphaSpending, method) {
+.getDesignFisherInner <- function(kMax, alpha, tolerance, criticalValues, scale, alpha0Vec, userAlphaSpending, method) {
     .Call(`_rpact_getDesignFisherTryCpp`, kMax, alpha, tolerance, criticalValues, scale, alpha0Vec, userAlphaSpending, method)
 }
 
-getGroupSequentialProbabilitiesCpp <- function(decisionMatrix, informationRates) {
+.getGroupSequentialProbabilitiesCpp <- function(decisionMatrix, informationRates) {
     .Call(`_rpact_getGroupSequentialProbabilitiesCpp`, decisionMatrix, informationRates)
 }
 
-getDesignGroupSequentialPampallonaTsiatisCpp <- function(tolerance, beta, alpha, kMax, deltaPT0, deltaPT1, informationRates, sided, bindingFutility) {
+.getDesignGroupSequentialPampallonaTsiatisCpp <- function(tolerance, beta, alpha, kMax, deltaPT0, deltaPT1, informationRates, sided, bindingFutility) {
     .Call(`_rpact_getDesignGroupSequentialPampallonaTsiatisCpp`, tolerance, beta, alpha, kMax, deltaPT0, deltaPT1, informationRates, sided, bindingFutility)
 }
 
-getDesignGroupSequentialUserDefinedAlphaSpendingCpp <- function(kMax, userAlphaSpending, sided, informationRates, bindingFutility, futilityBounds, tolerance) {
+.getDesignGroupSequentialUserDefinedAlphaSpendingCpp <- function(kMax, userAlphaSpending, sided, informationRates, bindingFutility, futilityBounds, tolerance) {
     .Call(`_rpact_getDesignGroupSequentialUserDefinedAlphaSpendingCpp`, kMax, userAlphaSpending, sided, informationRates, bindingFutility, futilityBounds, tolerance)
 }
 
-getDesignGroupSequentialAlphaSpendingCpp <- function(kMax, alpha, gammaA, typeOfDesign, sided, informationRates, bindingFutility, futilityBounds, tolerance) {
+.getDesignGroupSequentialAlphaSpendingCpp <- function(kMax, alpha, gammaA, typeOfDesign, sided, informationRates, bindingFutility, futilityBounds, tolerance) {
     .Call(`_rpact_getDesignGroupSequentialAlphaSpendingCpp`, kMax, alpha, gammaA, typeOfDesign, sided, informationRates, bindingFutility, futilityBounds, tolerance)
 }
 
-getDesignGroupSequentialDeltaWTCpp <- function(kMax, alpha, sided, informationRates, bindingFutility, futilityBounds, tolerance, deltaWT) {
+.getDesignGroupSequentialDeltaWTCpp <- function(kMax, alpha, sided, informationRates, bindingFutility, futilityBounds, tolerance, deltaWT) {
     .Call(`_rpact_getDesignGroupSequentialDeltaWTCpp`, kMax, alpha, sided, informationRates, bindingFutility, futilityBounds, tolerance, deltaWT)
 }
 
-getDesignGroupSequentialPocockCpp <- function(kMax, alpha, sided, informationRates, bindingFutility, futilityBounds, tolerance) {
+.getDesignGroupSequentialPocockCpp <- function(kMax, alpha, sided, informationRates, bindingFutility, futilityBounds, tolerance) {
     .Call(`_rpact_getDesignGroupSequentialPocockCpp`, kMax, alpha, sided, informationRates, bindingFutility, futilityBounds, tolerance)
 }
 
-getDesignGroupSequentialOBrienAndFlemingCpp <- function(kMax, alpha, sided, informationRates, bindingFutility, futilityBounds, tolerance) {
+.getDesignGroupSequentialOBrienAndFlemingCpp <- function(kMax, alpha, sided, informationRates, bindingFutility, futilityBounds, tolerance) {
     .Call(`_rpact_getDesignGroupSequentialOBrienAndFlemingCpp`, kMax, alpha, sided, informationRates, bindingFutility, futilityBounds, tolerance)
 }
 
-getDesignGroupSequentialBetaSpendingCpp <- function(criticalValues, kMax, userAlphaSpending, userBetaSpending, informationRates, bindingFutility, tolerance, typeOfDesign, typeBetaSpending, gammaA, gammaB, alpha, beta, sided, betaAdjustment, twoSidedPower) {
+.getDesignGroupSequentialBetaSpendingCpp <- function(criticalValues, kMax, userAlphaSpending, userBetaSpending, informationRates, bindingFutility, tolerance, typeOfDesign, typeBetaSpending, gammaA, gammaB, alpha, beta, sided, betaAdjustment, twoSidedPower) {
     .Call(`_rpact_getDesignGroupSequentialBetaSpendingCpp`, criticalValues, kMax, userAlphaSpending, userBetaSpending, informationRates, bindingFutility, tolerance, typeOfDesign, typeBetaSpending, gammaA, gammaB, alpha, beta, sided, betaAdjustment, twoSidedPower)
 }
 
-getDesignGroupSequentialUserDefinedBetaSpendingCpp <- function(criticalValues, kMax, userAlphaSpending, userBetaSpending, sided, informationRates, bindingFutility, tolerance, typeOfDesign, gammaA, alpha, betaAdjustment, twoSidedPower) {
+.getDesignGroupSequentialUserDefinedBetaSpendingCpp <- function(criticalValues, kMax, userAlphaSpending, userBetaSpending, sided, informationRates, bindingFutility, tolerance, typeOfDesign, gammaA, alpha, betaAdjustment, twoSidedPower) {
     .Call(`_rpact_getDesignGroupSequentialUserDefinedBetaSpendingCpp`, criticalValues, kMax, userAlphaSpending, userBetaSpending, sided, informationRates, bindingFutility, tolerance, typeOfDesign, gammaA, alpha, betaAdjustment, twoSidedPower)
 }
 
-getSimulationMeansLoopCpp <- function(alternative, kMax, maxNumberOfIterations, designNumber, informationRates, futilityBounds, alpha0Vec, criticalValues, meanRatio, thetaH0, stDev, groups, normalApproximation, plannedSubjects, directionUpper, allocationRatioPlanned, minNumberOfSubjectsPerStage, maxNumberOfSubjectsPerStage, conditionalPower, thetaH1, stDevH1, calcSubjectsFunctionType, calcSubjectsFunctionR, calcSubjectsFunctionCpp) {
+.getSimulationMeansLoopCpp <- function(alternative, kMax, maxNumberOfIterations, designNumber, informationRates, futilityBounds, alpha0Vec, criticalValues, meanRatio, thetaH0, stDev, groups, normalApproximation, plannedSubjects, directionUpper, allocationRatioPlanned, minNumberOfSubjectsPerStage, maxNumberOfSubjectsPerStage, conditionalPower, thetaH1, stDevH1, calcSubjectsFunctionType, calcSubjectsFunctionR, calcSubjectsFunctionCpp) {
     .Call(`_rpact_getSimulationMeansLoopCpp`, alternative, kMax, maxNumberOfIterations, designNumber, informationRates, futilityBounds, alpha0Vec, criticalValues, meanRatio, thetaH0, stDev, groups, normalApproximation, plannedSubjects, directionUpper, allocationRatioPlanned, minNumberOfSubjectsPerStage, maxNumberOfSubjectsPerStage, conditionalPower, thetaH1, stDevH1, calcSubjectsFunctionType, calcSubjectsFunctionR, calcSubjectsFunctionCpp)
 }
 
-getSimulationRatesCpp <- function(kMax, informationRates, criticalValues, pi1, pi2, maxNumberOfIterations, designNumber, groups, futilityBounds, alpha0Vec, minNumberOfSubjectsPerStage, maxNumberOfSubjectsPerStage, conditionalPower, pi1H1, pi2H1, normalApproximation, plannedSubjects, directionUpper, allocationRatioPlanned, riskRatio, thetaH0, calcSubjectsFunctionType, calcSubjectsFunctionR, calcSubjectsFunctionCpp) {
+.getSimulationRatesCpp <- function(kMax, informationRates, criticalValues, pi1, pi2, maxNumberOfIterations, designNumber, groups, futilityBounds, alpha0Vec, minNumberOfSubjectsPerStage, maxNumberOfSubjectsPerStage, conditionalPower, pi1H1, pi2H1, normalApproximation, plannedSubjects, directionUpper, allocationRatioPlanned, riskRatio, thetaH0, calcSubjectsFunctionType, calcSubjectsFunctionR, calcSubjectsFunctionCpp) {
     .Call(`_rpact_getSimulationRatesCpp`, kMax, informationRates, criticalValues, pi1, pi2, maxNumberOfIterations, designNumber, groups, futilityBounds, alpha0Vec, minNumberOfSubjectsPerStage, maxNumberOfSubjectsPerStage, conditionalPower, pi1H1, pi2H1, normalApproximation, plannedSubjects, directionUpper, allocationRatioPlanned, riskRatio, thetaH0, calcSubjectsFunctionType, calcSubjectsFunctionR, calcSubjectsFunctionCpp)
 }
 
-getSimulationSurvivalCpp <- function(designNumber, kMax, sided, criticalValues, informationRates, conditionalPower, plannedEvents, thetaH1, minNumberOfEventsPerStage, maxNumberOfEventsPerStage, directionUpper, allocationRatioPlanned, accrualTime, treatmentGroup, thetaH0, futilityBounds, alpha0Vec, pi1Vec, pi2, eventTime, piecewiseSurvivalTime, cdfValues1, cdfValues2, lambdaVec1, lambdaVec2, phi, maxNumberOfSubjects, maxNumberOfIterations, maxNumberOfRawDatasetsPerStage, kappa, calcEventsFunctionType, calcEventsFunctionR, calcEventsFunctionCpp) {
+.getSimulationSurvivalCpp <- function(designNumber, kMax, sided, criticalValues, informationRates, conditionalPower, plannedEvents, thetaH1, minNumberOfEventsPerStage, maxNumberOfEventsPerStage, directionUpper, allocationRatioPlanned, accrualTime, treatmentGroup, thetaH0, futilityBounds, alpha0Vec, pi1Vec, pi2, eventTime, piecewiseSurvivalTime, cdfValues1, cdfValues2, lambdaVec1, lambdaVec2, phi, maxNumberOfSubjects, maxNumberOfIterations, maxNumberOfRawDatasetsPerStage, kappa, calcEventsFunctionType, calcEventsFunctionR, calcEventsFunctionCpp) {
     .Call(`_rpact_getSimulationSurvivalCpp`, designNumber, kMax, sided, criticalValues, informationRates, conditionalPower, plannedEvents, thetaH1, minNumberOfEventsPerStage, maxNumberOfEventsPerStage, directionUpper, allocationRatioPlanned, accrualTime, treatmentGroup, thetaH0, futilityBounds, alpha0Vec, pi1Vec, pi2, eventTime, piecewiseSurvivalTime, cdfValues1, cdfValues2, lambdaVec1, lambdaVec2, phi, maxNumberOfSubjects, maxNumberOfIterations, maxNumberOfRawDatasetsPerStage, kappa, calcEventsFunctionType, calcEventsFunctionR, calcEventsFunctionCpp)
 }
 

R/class_analysis_dataset.R

@@ -13,8 +13,8 @@
 ## |
 ## |  Contact us for information about our services: [email protected]
 ## |
-## |  File version: $Revision: 7139 $
-## |  Last changed: $Date: 2023-06-28 08:15:31 +0200 (Mi, 28 Jun 2023) $
+## |  File version: $Revision: 7554 $
+## |  Last changed: $Date: 2024-01-12 10:19:05 +0100 (Fr, 12 Jan 2024) $
 ## |  Last changed by: $Author: pahlke $
 ## |
 
@@ -107,7 +107,7 @@ C_KEY_WORDS <- c(
 #' @param fileEncoding character string: if non-empty declares the encoding used on a file
 #'        (not a connection) so the character data can be re-encoded.
 #'        See the 'Encoding' section of the help for file, the 'R Data Import/Export Manual' and 'Note'.
-#' @param ... Further arguments to be passed to code{\link[utils]{read.table}}.
+#' @param ... Further arguments to be passed to \code{\link[utils]{read.table}}.
 #'
 #' @details
 #' \code{readDataset} is a wrapper function that uses \code{\link[utils]{read.table}} to read the

R/class_analysis_stage_results.R

@@ -13,8 +13,8 @@
 ## |
 ## |  Contact us for information about our services: [email protected]
 ## |
-## |  File version: $Revision: 7126 $
-## |  Last changed: $Date: 2023-06-23 14:26:39 +0200 (Fr, 23 Jun 2023) $
+## |  File version: $Revision: 7554 $
+## |  Last changed: $Date: 2024-01-12 10:19:05 +0100 (Fr, 12 Jan 2024) $
 ## |  Last changed by: $Author: pahlke $
 ## |
 
@@ -966,8 +966,7 @@ StageResultsMultiArmSurvival <- setRefClass("StageResultsMultiArmSurvival",
         directionUpper = "logical"
     ),
     methods = list(
-        initialize = function(design, dataInput, ...,
-                normalApproximation = FALSE) {
+        initialize = function(design, dataInput, ...) {
             callSuper(.design = design, .dataInput = dataInput, ...)
             init(design = design, dataInput = dataInput)
 
@@ -1653,7 +1652,7 @@ plot.StageResults <- function(x, y, ..., type = 1L,
         p <- p + ggplot2::geom_errorbar(
             data = data,
             ggplot2::aes(ymin = .data[["lower"]], ymax = .data[["upper"]]),
-            width = 0.15, position = pd, size = 0.8
+            width = 0.15, position = pd, linewidth = 0.8
         )
         p <- p + ggplot2::geom_line(position = pd, linetype = "longdash")
         p <- p + ggplot2::geom_point(position = pd, size = 2.0)