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 = "stephen.schueuerhuis@charite.de",
     	role = c("ctb")
+	),
+    person(
+    	given = "Tobias", 
+    	family = "Muetze", 
+    	email = "tobias.muetze@novartis.com",
+    	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: info@rpact.com
 ## |
-## |  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: info@rpact.com
 ## |
-## |  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)