High-Performance Open-Source Archive
testPackage() with additional options for
report customization and metadata handling. A new argument
reportFileBaseName allows explicit control over the base
name of all generated report files.testPackage() now supports structured metadata via the
new arguments metaData and metaDataFile. Users
can either pass a named list of key–value pairs directly or provide an
external text file in KEY=VALUE format. This metadata is
included in the generated test reports and supports reproducible,
auditable installation qualification and validation workflows.writeKeyValueFile() and readKeyValueFile(),
for writing and reading human-editable key–value metadata files
(KEY=VALUE, INI/.env-like format). These functions are
implemented using base R only, support UTF-8 encoding, comments, and
robust parsing, and are intended for use in regulated environments.testPackage() to support structured metadata handling via
the metaData and metaDataFile arguments,
enabling reproducible and auditable inclusion of environment- and
system-specific information in installation qualification and validation
reports.identical() for user-defined parameter
checks.getHazardRatioByPi() to
return the hazard ratio directly derived from the corresponding rate
parameters, i.e., the hazard ratio is defined as lambda1 /
lambda2; the Weibull shape parameter kappa
controls time-dependence only.finalPValues for two-sided
group-sequential designs in specific edge cases (see issue #114).getFutilityBounds() converts futility
bounds between different scales such as z-value, p-value, conditional
power, predictive power, reverse conditional power, and effect
estimate.futilityBoundsScale in
getDesignInverseNormal() and
getDesignGroupSequential() allows specifying futility
bounds directly on alternative scales (e.g. p-value), which are
internally converted as needed.alpha0Scale in
getDesignFisher() allows specifying alpha0 bounds directly
on alternative scales, which are internally converted as needed.maxNumberOfRawDatasetsPerStage in
survival clarifiedgetTestLabel() introducedefficacyStops 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 plotgetSampleSizeMeans(), getPowerMeans(),
getSimulationMeans(): For two-armed trials, it is allowed
to specify the standard deviations (stDev) separately,
i.e., as vector with two elements.getDesignConditionalDunnett()) 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 = “WToptimum”as.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
“cumulative” 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 ‘userAlphaSpending’ fixedgetSimulationEnrichmentMeans(),
getSimulationEnrichmentRates(),
getSimulationEnrichmentSurvival() available for simulation
of enrichment designs; note that this is a novel implementation, hence
experimentalgetDesignGroupSequential() /
getDesignInverseNormal(): new typeOfDesign =
“noEarlyEfficacy” 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
‘connection’ the owners of the rpact validation documentation can enter
a ‘token’ and a ‘secret’ 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 “effectDifference” changed to “effectEstimate”getSimulation[MultiArm][Means/Rates/Survival]():
argument ‘showStatistics’ 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
‘accrualIntensityType’ addedgetSampleSizeSurvival(): field ‘studyDurationH1’ in
result object was replaced by ‘studyDuration’, i.e., ‘studyDurationH1’
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 ‘showStatistics’ addedplot(x, type = "all")plot(x, type = c(1, 3))plot(x, grid = 1): new plot argument ‘grid’ 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 ‘.stageResults’
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 ‘.design’ object to access them, e.g.,
results$.design$informationRatesplot(x, showSource = TRUE) improved for all rpact
result objects xgetSimulationRates(): issue for futility stopping for
Fisher’s 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 ‘followUpTime’
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 ‘effectSizes’ 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 ‘maxNumberOfSubjects’ for given ‘followUpTime’
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 ‘kind’ and ‘normal.kind’ were
specified as follows: kind = “Mersenne-Twister”, normal.kind =
“Inversion”readDatasets() fixed: variable names ‘group’ and
‘groups’ are now acceptedgetSampleSizeSurvival(): argument ‘maxNumberOfPatients’
was renamed in ‘maxNumberOfSubjects’
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