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rpact

Confirmatory Adaptive Clinical Trial Design, Simulation, and Analysis.

Functional Range

  • Fixed sample design and designs with interim analysis stages
  • Sample size and power calculation for
    • means (continuous endpoint)
    • rates (binary endpoint)
    • survival trials with flexible recruitment and survival time options
    • count data
  • Simulation tool for means, rates, and survival data
    • Assessment of adaptive sample size/event number recalculations based on conditional power
    • Assessment of treatment selection strategies in multi-arm trials
  • Adaptive analysis of means, rates, and survival data
  • Adaptive designs and analysis for multi-arm trials
  • Adaptive analysis and simulation tools for enrichment design testing means, rates, and hazard ratios
  • Automatic boundary recalculations during the trial for analysis with alpha spending approach, including under- and over-running

Installation

Install the latest CRAN release via

install.packages("rpact")

Development version

To use a feature from the development version, you can install the development version of rpact from GitHub.

# install.packages("pak")
pak::pak("rpact-com/rpact")

Documentation

The documentation is hosted at www.rpact.org

Vignettes

The vignettes are hosted at www.rpact.org/vignettes

The rpact user group

The rpact project has an active user group consisting of decision-makers and users from the pharmaceutical industry and CROs, who meet regularly and, e.g., discuss best practices.

We invite you to be part of the rpact user group: benefit from know-how, shape open source development in Pharma!

Use on corporate computer systems

Please contact us to learn how to use rpact on FDA/GxP-compliant validated corporate computer systems and how to get a copy of the formal validation documentation that is customized and licensed for exclusive use by your company, e.g., to fulfill regulatory requirements. The validation documentation contains the personal access data for performing the installation qualification with testPackage().

www.rpact.com/contact

About

  • rpact is a comprehensive validated[^1] R package for clinical research which
    • enables the design and analysis of confirmatory adaptive group sequential designs
    • is a powerful sample size calculator
    • is a free of charge open-source software licensed under LGPL-3
    • particularly, implements the methods described in the recent monograph by Wassmer and Brannath (2016)

For more information please visit www.rpact.org

  • RPACT is a company which offers
    • enterprise R/Shiny software development services
    • technical support for the rpact package
    • consultancy and user training for scientists using R
    • validated software solutions and R package development for clinical research

For more information please visit www.rpact.com

[^1]: The rpact validation documentation is available exclusively for our customers and supporting members. For more information visit www.rpact.com/services/sla

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Version

Install

install.packages('rpact')

Monthly Downloads

2,231

Version

3.5.0

License

LGPL-3

Issues

Pull Requests

Stars

Forks

Maintainer

Friedrich Pahlke

Last Published

January 25th, 2024

Functions in rpact (3.5.0)

AnalysisResultsConditionalDunnett

Analysis Results Multi-Arm Conditional Dunnett
AnalysisResultsEnrichment

Basic Class for Analysis Results Enrichment
AnalysisResultsEnrichmentInverseNormal

Analysis Results Enrichment Inverse Normal
AnalysisResultsFisher

Analysis Results Fisher
ConditionalPowerResultsEnrichmentRates

Conditional Power Results Enrichment Rates
AccrualTime

Accrual Time
AnalysisResults

Basic Class for Analysis Results
AnalysisResultsMultiArmFisher-class

Analysis Results Multi-Arm Fisher
AnalysisResultsMultiArmInverseNormal

Analysis Results Multi-Arm Inverse Normal
ConditionalPowerResultsMeans

Conditional Power Results Means
ConditionalPowerResultsRates

Conditional Power Results Rates
AnalysisResultsGroupSequential

Analysis Results Group Sequential
AnalysisResultsMultiArm

Basic Class for Analysis Results Multi-Arm
PerformanceScore

Performance Score
ConditionalPowerResultsSurvival

Conditional Power Results Survival
ConditionalPowerResults

Conditional Power Results
Dataset

Dataset
ConditionalPowerResultsEnrichmentMeans

Conditional Power Results Enrichment Means
PiecewiseSurvivalTime

Piecewise Exponential Survival Time
NumberOfSubjects

Number Of Subjects
SimulationResults

Class for Simulation Results
ParameterSet

Parameter Set
DatasetRates

Dataset of Rates
DatasetSurvival

Dataset of Survival Data
StageResultsMultiArmRates

Stage Results Multi Arm Rates
DatasetMeans

Dataset of Means
StageResultsMultiArmSurvival

Stage Results Multi Arm Survival
TrialDesignInverseNormal

Inverse Normal Design
PlotSettings

Plot Settings
TrialDesignPlan

Basic Trial Design Plan
AnalysisResultsMultiHypotheses

Basic Class for Analysis Results Multi-Hypotheses
PowerAndAverageSampleNumberResult

Power and Average Sample Number Result
SimulationResultsEnrichmentMeans

Class for Simulation Results Enrichment Means
TrialDesignPlanCountData

Trial Design Plan Count Data
SimulationResultsMultiArmRates

Class for Simulation Results Multi-Arm Rates
SimulationResultsEnrichmentRates

Class for Simulation Results Enrichment Rates
ClosedCombinationTestResults

Analysis Results Closed Combination Test
SimulationResultsEnrichmentSurvival

Class for Simulation Results Enrichment Survival
EventProbabilities

Event Probabilities
SimulationResultsMultiArmSurvival

Class for Simulation Results Multi-Arm Survival
SimulationResultsRates

Class for Simulation Results Rates
FieldSet

Field Set
SimulationResultsSurvival

Class for Simulation Results Survival
as.data.frame.ParameterSet

Coerce Parameter Set to a Data Frame
StageResults

Basic Stage Results
as.data.frame.PowerAndAverageSampleNumberResult

Coerce Power And Average Sample Number Result to a Data Frame
TrialDesignPlanMeans

Trial Design Plan Means
dataEnrichmentMeans

Enrichment Dataset of Means
SimulationResultsMeans

Class for Simulation Results Means
dataEnrichmentMeansStratified

Stratified Enrichment Dataset of Means
StageResultsEnrichmentMeans

Stage Results Enrichment Means
TrialDesignCharacteristics

Trial Design Characteristics
SimulationResultsMultiArmMeans

Class for Simulation Results Multi-Arm Means
StageResultsRates

Stage Results of Rates
dataRates

One-Arm Dataset of Rates
StageResultsMeans

Stage Results of Means
dataSurvival

One-Arm Dataset of Survival Data
StageResultsMultiArmMeans

Stage Results Multi Arm Means
StageResultsEnrichmentRates

Stage Results Enrichment Rates
getDataset

Get Dataset
TrialDesignConditionalDunnett

Conditional Dunnett Design
StageResultsSurvival

Stage Results of Survival Data
as.data.frame.StageResults

Coerce Stage Results to a Data Frame
SummaryFactory

Summary Factory
getDesignCharacteristics

Get Design Characteristics
as.data.frame.TrialDesignSet

Coerce Trial Design Set to a Data Frame
as.matrix.FieldSet

Coerce Field Set to a Matrix
as.data.frame.TrialDesign

Coerce TrialDesign to a Data Frame
dataEnrichmentRates

Enrichment Dataset of Rates
getDesignGroupSequential

Get Design Group Sequential
dataMultiArmRates

Multi-Arm Dataset of Rates
StageResultsEnrichmentSurvival

Stage Results Enrichment Survival
dataEnrichmentRatesStratified

Stratified Enrichment Dataset of Rates
TrialDesign

Basic Trial Design
getDesignInverseNormal

Get Design Inverse Normal
TrialDesignFisher

Fisher Design
TrialDesignPlanRates

Trial Design Plan Rates
TrialDesignGroupSequential

Group Sequential Design
dataMeans

One-Arm Dataset of Means
getParameterCaption

Get Parameter Caption
dataMultiArmSurvival

Multi-Arm Dataset of Survival Data
dataMultiArmMeans

Multi-Arm Dataset of Means
plotTypes

Get Available Plot Types
TrialDesignPlanSurvival

Trial Design Plan Survival
getClosedConditionalDunnettTestResults

Get Closed Conditional Dunnett Test Results
getPowerCounts

Get Power Counts
getConditionalPower

Get Conditional Power
getGroupSequentialProbabilities

Get Group Sequential Probabilities
getLambdaStepFunction

Get Lambda Step Function
as251Normal

Algorithm AS 251: Normal Distribution
getPowerMeans

Get Power Means
getParameterName

Get Parameter Name
as251StudentT

Algorithm AS 251: Student T Distribution
getNumberOfSubjects

Get Number Of Subjects
getAccrualTime

Get Accrual Time
TrialDesignSet

Class for trial design sets.
as.data.frame.AnalysisResults

Coerce AnalysisResults to a Data Frame
getAnalysisResults

Get Analysis Results
as.data.frame.TrialDesignCharacteristics

Coerce TrialDesignCharacteristics to a Data Frame
as.data.frame.TrialDesignPlan

Coerce Trial Design Plan to a Data Frame
getClosedCombinationTestResults

Get Closed Combination Test Results
getDesignConditionalDunnett

Get Design Conditional Dunnett Test
dataEnrichmentSurvival

Enrichment Dataset of Survival Data
getDesignFisher

Get Design Fisher
getSampleSizeMeans

Get Sample Size Means
getSampleSizeRates

Get Sample Size Rates
getDesignSet

Get Design Set
dataEnrichmentSurvivalStratified

Stratified Enrichment Dataset of Survival Data
getEventProbabilities

Get Event Probabilities
getConditionalRejectionProbabilities

Get Conditional Rejection Probabilities
getData

Get Simulation Data
getPlotSettings

Get Plot Settings
getLogLevel

Get Log Level
rcmd

Get Object R Code
getLongFormat

Get Long Format
getFinalConfidenceInterval

Get Final Confidence Interval
getPowerAndAverageSampleNumber

Get Power And Average Sample Number
getRepeatedPValues

Get Repeated P Values
getRawData

Get Simulation Raw Data for Survival
getFinalPValue

Get Final P Value
getRepeatedConfidenceIntervals

Get Repeated Confidence Intervals
getPerformanceScore

Get Performance Score
getSampleSizeCounts

Get Sample Size Counts
getObservedInformationRates

Get Observed Information Rates
getPiecewiseSurvivalTime

Get Piecewise Survival Time
getOutputFormat

Get Output Format
getPowerRates

Get Power Rates
getPowerSurvival

Get Power Survival
AnalysisResultsInverseNormal

Analysis Results Inverse Normal
AnalysisResultsEnrichmentFisher

Analysis Results Enrichment Fisher