efficacyStops and futilityStops parameter added (issue #88)stdErrorEstimate ("pooled" or "unpooled") added for calculation of final confidence intervals in two-sample situation for ratestestPackage() returns an InstallationQualificationResult objectgetSimulationCounts() (issue #84)getSimulationMultiArmMeans(), getSimulationMultiArmRates(), and getSimulationMultiArmSurvival() it is now possible to specify a parameter doseLevels to define the dose levels for a linear or sigmoidEmax dose-response relationship (see feature request #63)getSampleSizeMeans(), getPowerMeans(), and getSimulationMeans() functions, see enhancement #70testPackage() produces a comprehensive installation qualification report in html and pdf format (see new vignette Installation Qualification of rpact)setupPackageTests() sets up the package tests by downloading the test files and copying them to the rpact installation directorysaveOptions() saves the current rpact options to a configuration fileresetOptions() resets the rpact options to their default valuesconservative added to getSampleSizeRates() function, see enhancement #39options("rpact.plot.show.futility.on.pvalue.scale" = TRUE) or argument showFutilityBounds = TRUE, see enhancement #79options("rpact.plot.show.beta.spent" = TRUE) or argument showBetaSpent = TRUE, see enhancement #80. Furthermore, options("rpact.plot.show.alpha.spent" = FALSE) or argument showAlphaSpent = FALSE can be used to show only beta-spending in the plotgetDesignConditionalDunnett()) in analysis tool is fixed.maxInformation improved (see enhancement #65)getObjectRCode() improved (see #81)testPackage(): additional warning details will be added to the test report if warnings exist* Issue #61 fixedgetPiecewiseSurvivalTime() improvedinformationRates, userAlphaSpending, and userBetaSpendinggetSimulationCounts() can be used to perform power simulations for clinical trials with negative binomial distributed count data. The function returns the simulated power, stopping probabilities, conditional power, and expected sample size for testing mean rates for negative binomial distributed event numbers in the two treatment groups testing situationgetDesignGroupSequential(), getDesignInverseNormal(), and getDesignFisher() now support the argument directionUpper to specify the direction of the alternative for one-sided testing early at the design phase, see enhancement #26getSampleSizeCounts() and getPowerCounts() output boundary values also on the treatment effect scale, see enhancement #40fetch() and obtain() functions can be used to extract multiple parameters from an rpact result object and support various output formatskable() for rpact result objects marked as deprecated, as the formatting and display will be handled automatically by rpactggplot2 changed from 2.2.0 to 3.2.0directionUpper = FALSE has no influence in simulation for testing rates in one-sample situationgetPerformanceScore() for sample size recalculation rules to the setting of binary endpoints according to Bokelmann et al. (2024)getSimulationMultiArmMeans(), getSimulationMultiArmRates(), and getSimulationMultiArmSurvival() functions now support an enhanced selectArmsFunction argument. Previously, only effectVector and stage were allowed as arguments. Now, users can optionally utilize additional arguments for more powerful custom function implementations, including conditionalPower, conditionalCriticalValue, plannedSubjects/plannedEvents, allocationRatioPlanned, selectedArms, thetaH1 (for means and survival), stDevH1 (for means), overallEffects, and for rates additionally: piTreatmentsH1, piControlH1, overallRates, and overallRatesControlgetSimulationEnrichmentMeans(), getSimulationEnrichmentRates(), and getSimulationEnrichmentSurvival(). Specifically, support for population selection with selectPopulationsFunction argument based on predictive/posterior probabilities added (see #32)fetch() and obtain() functions can be used to extract a single parameter from an rpact result object, which is useful for writing pipe-operator linked commands.parameterNames and .parameterFormatFunctions were removed from all rpact result objects in favor of a more efficient solutiongetSampleSizeSurvival() / getPowerSurvival():
eventsPerStage replaced by cumulativeEventsPerStagesingleEventsPerStage addedgetSimulationSurvival():
eventsPerStage replaced by singleEventsPerStageoverallEventsPerStage replaced by cumulativeEventsPerStagegetSimulationMultiArmSurvival():
eventsPerStage replaced by cumulativeEventsPerStagesingleNumberOfEventsPerStage replaced by singleEventsPerArmAndStagesingleEventsPerStage addedgetSimulationEnrichmentSurvival():
singleNumberOfEventsPerStage replaced by singleEventsPerSubsetAndStagecovr and uploads the results to codecov.iogetSampleSizeCounts() 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).mvnprd, mvstud, as251Normal, and as251StudentTmnormt dependency has been removedtheta can be used for plotting of sample size and power resultsgetPerformanceScore() calculates the conditional performance score, its sub-scores and components according to Herrmann et al. (2020) for a given simulation result from a two-stage designallocationRatioPlanned for simulating multi-arm and enrichment designs can be a vector of length kMax, the number of stagesgetObjectRCode() (short: rcmd()): with the new arguments pipeOperator and output many new output variants can be specified, e.g., the native R pipe operator or the magrittr pipe operator can be usedknitr::knit_print for all result objects implemented and automatic code chunk option results = 'asis' activateddf <= 500 because of erroneous results in mnormt package otherwise. For df > 500, multivariate normal distribution is usedomega to chi in class TrialDesignPlanSurvivalsapply removed from C++ code to stop deprecated warnings on r-devel-linux-x86_64-fedora-clangallocationRatioPlanned for simulating means and rates for a two treatment groups design can be a vector of length kMax, the number of stagescalcSubjectsFunction can be used in C++ version for simulating means and ratescalcEventsFunction added in getSimulationSurvival()getPerformanceScore() added: calculates the performance score for simulation means results (1 and 2 groups; 2 stages)getDataset() to enable pipe syntax for analysis, e.g., getDesignGroupSequential() |> getDataset(dataMeans) |> getAnalysisResults()SystemRequirements: C++11 added to DESCRIPTION to enable C++ 11 compilation on R 3.xbetaAdjustment can also be used in getDesignInverseNormal()subsets removed from result of getWideFormat() for non-enrichment datasetspopulations in getSimulationEnrichmentMeans(), getSimulationEnrichmentRates(), and getSimulationEnrichmentSurvival() has been removed since it is always derived from effectListgetSimulationEnrichmentRates() for calculated non-integer number of subjectsgetRawData(): the resulting data.frame now contains the correct stopStage and lastObservationTime (formerly observationTime)deltaWT is provided with three decimal points for typeOfDesign = âWToptimumâas.data.frame functions improvedgetSimulationMultiArmSurvival(): single stage treatment arm specific event numbers account for selection proceduregetSimulationEnrichmentRates() and getSimulationEnrichmentSurvival()getDesignCharacteristics()getSimulationSurvival(): the result object now contains the new parameter overallEventsPerStage, which contains the values previously given in eventsPerStage (it was âcumulativeâ by mistake); eventsPerStage contains now the non-cumulative values as expectedstats::qnorm(1e-323) to stats::qnorm(1e-100)getAnalysisResults(): issue with zero values in the argument âuserAlphaSpendingâ fixedgetSimulationEnrichmentMeans(), getSimulationEnrichmentRates(), getSimulationEnrichmentSurvival() available for simulation of enrichment designs; note that this is a novel implementation, hence experimentalgetDesignGroupSequential() / getDesignInverseNormal(): new typeOfDesign = ânoEarlyEfficacyâ addedgetSimulationSurvival(): bug fixed for accruallIntensity = 0 at some accrual intervalsgetSimulationMultiArmMeans(), getSimulationMultiArmRates(), and getSimulationMultiArmSurvival()testPackage(): a problem with downloading full set of unit tests under Debian/Linux has been fixedkable() improved: optional knitr::kable arguments enabled, e.g., formatqnorm() calculations improvedas.data.frame()getAnalysisResults() generalized for enrichment designs; function getDataset() generalized for entering stratified data; manual extended for enrichment designsgetAnalysisResults()getObjectRCode() (short: rcmd()) returns the original R command which produced any rpact result object, including all dependenciesgetWideFormat() and getLongFormat() return a dataset object in wide format (unstacked) or long format (narrow, stacked)kable() returns the output of an rpact result object formatted in Markdown.t() returns the transpose of an rpact result objectgetDesignFisher() fixed: getDesignFisher(method = "noInteraction", kMax = 3) and getDesignFisher(method = "noInteraction") produced different resultstestPackage(): the default call is now running only a small subset of all available unit tests; with the new argument âconnectionâ the owners of the rpact validation documentation can enter a âtokenâ and a âsecretâ to get full access to all unit testsgetSampleSizeSurvival(), getSimulationSurvival(), getNumberOfSubjects(), and getEventProbabilities()getParameterCaption() and getParameterName() implementedas.matrix() improved for several result objectsgetAvailablePlotTypes() for sample size and power results fixedgetDesignFisher(kMax = 1) in getSimulationMultiArm...() fixedgetSimulationMultiArmSurvival(): correlation of log-rank statistics revised and improvedgetSimulationMultiArmMeans(): name of the first effectMeasure option âeffectDifferenceâ changed to âeffectEstimateâgetSimulation[MultiArm][Means/Rates/Survival](): argument âshowStatisticsâ now works correctly and is consistently FALSE by default for multi-arm and non-multi-armgetSimulation[MultiArm]Survival(): generic function summary() improvedgetAnalysisResults(): generic function summary() improvedgetAccrualTime(): improved and new argument âaccrualIntensityTypeâ addedgetSampleSizeSurvival(): field âstudyDurationH1â in result object was replaced by âstudyDurationâ, i.e., âstudyDurationH1â is deprecated and will be removed in future versionsgetSimulationMultiArmSurvival(): plannedEvents redefined as overall events over treatment armsgetStageResults(): element overallPooledStDevs added; print output improvedgetSampleSizeSurvival() with user defined lambdas with different lengths: issue fixedsummary() improved for several result objectstestPackage() improvedgetSimulationMultiArm[Means/Rates/Survival](): stage index corrected for user defined calcSubjectsFunction or calcEventsFunctiongetSimulationMultiArmRates(): adjustment for identical simulated rates to account for tiesgetSimulationMultiArmSurvival(): corrected correlation of test statisticsgetSimulationRates(): exact versions for testing a rate (one-sample case) and equality of rates (two-sample case)getEventProbabilities(): plot of result objectgetNumberOfSubjects(): plot of result objectplot(design1, design2)getSimulationMeans(): thetaH1 and stDevH1 can be specified for assessment of sample size recalculation (replaces thetaStandardized)getSimulationSurvival(): separate p-values added to the aggregated simulation data for Fisher designsgetSimulationMeans(), getSimulationRates(): Cumulated number of subjects integrated in getData objectgetSimulation[MultiArm][Means/Rates/Survival](): new logical argument âshowStatisticsâ addedplot(x, type = "all")plot(x, type = c(1, 3))plot(x, grid = 1): new plot argument âgridâ enables the plotting of 2 or more plots in one graphicgetAnalysisResults(): list output implemented analogous to the output of all other rpact objectsgetAnalysisResults(): the following stage result arguments were removed from result object because they were redundant: effectSizes, testStatistics, and pValues. Please use the â.stageResultsâ object to access them, e.g., results$.stageResults$effectSizesgetAnalysisResults(): the following design arguments were removed from result object because they were redundant: stages, informationRates, criticalValues, futilityBounds, alphaSpent, and stageLevels. Please use the â.designâ object to access them, e.g., results$.design$informationRatesplot(x, showSource = TRUE) improved for all rpact result objects xgetSimulationRates(): issue for futility stopping for Fisherâs combination test fixedgetSimulationSurvival(): issue for expected number of events fixedgetSimulationSurvival(): if eventsNotAchieved > 0, rejection/futility rate and analysis time is estimated for valid simulation runsgetSimulationSurvival(): output improved for lambda1/median1/hazardRatio with length > 1getSampleSizeSurvival(): calculation of the maximum number of subjects given the provided argument âfollowUpTimeâ improvedgetPiecewiseSurvivalTime(): delayed response via list-based piecewiseSurvivalTime definition enabledgetAccrualTime() / getSimulationSurvival(): issue with the calculation of absolute accrual intensity by given relative accrual intensity fixedgetRawData(): issue for multiple pi1 solvedgetAnalysisSurvival(): calculation of stage wise results not more in getStageResultsgetStageResults(): the calculation of âeffectSizesâ for survival data and thetaH0 != 1 was correctedgetDataset() of survival data: issue with the internal storage of log ranks fixedgetSampleSizeSurvival() with piecewise survival time: issue with calculation of âmaxNumberOfSubjectsâ for given âfollowUpTimeâ fixedsummary() improvedgetSampleSizeSurvival() with given maxNumberOfSubjects improvedget[SampleSize/Power]Survival() for Kappa != 1 improvedsummary() for getDesign[...]() fixedsummary() fixed for getSampleSize[...]() and getPower[...]()summary() implemented for getDesign[...](), getSampleSize[...](), getPower[...](), and getSimulation[...]() results: a simple boundary summary will be displayedgetDesign[...](), getSampleSize[...](), getPower[...](), and getSimulation[...]() resultsgetStageResults() improvedgetAccrualTime() improvedgetSampleSizeSurvival() improved: numeric search for accrualTime if followUpTime is givenset.seed() calls âkindâ and ânormal.kindâ were specified as follows: kind = âMersenne-Twisterâ, normal.kind = âInversionâreadDatasets() fixed: variable names âgroupâ and âgroupsâ are now acceptedgetSampleSizeSurvival(): argument âmaxNumberOfPatientsâ was renamed in âmaxNumberOfSubjectsâRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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