Getting started with 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, survival data, and count 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

Learn to use rpact

We recommend three ways to learn how to use rpact:

Use RPACT Cloud: rpact.cloud

Use the Vignettes: rpact.org/vignettes

Book a training: rpact.com

RPACT Cloud

A graphical user interface (GUI) for the rpact R package, designed to simplify its use through an intuitive interface: rpact.cloud

Vignettes

Developed for practical use: our collection of practical examples and use-cases, the so-called rpact vignettes, are hosted at rpact.org/vignettes

User Concept

Workflow

Everything is starting with a design, e.g.: design <- getDesignGroupSequential()
Find the optimal design parameters with help of rpact comparison tools: getDesignSet
Calculate the required sample size, e.g.: getSampleSizeMeans(), getPowerMeans()
Simulate specific characteristics of an adaptive design, e.g.: getSimulationMeans()
Collect your data, import it into R and create a dataset: data <- getDataset()
Analyze your data: getAnalysisResults(design, data)

Focus on Usability

The most important rpact functions have intuitive names:

getDesign[GroupSequential/InverseNormal/Fisher]()
getDesignCharacteristics()
getSampleSize[Means/Rates/Survival/Counts]()
getPower[Means/Rates/Survival/Counts]()
getSimulation[MultiArm/Enrichment][Means/Rates/Survival/Counts]()
getDataset()
getAnalysisResults()
getStageResults()

RStudio / Positron / Eclipse: auto code completion makes it easy to use these functions.

R generics

In general, everything runs with the R standard functions which are always present in R: so-called R generics, e.g., print, summary, plot, as.data.frame, names, length

Utilities

Several utility functions are available, e.g.

getAccrualTime()
getPiecewiseSurvivalTime()
getNumberOfSubjects()
getEventProbabilities()
getPiecewiseExponentialDistribution()
survival helper functions for conversion of pi, lambda and median, e.g., getLambdaByMedian()
testPackage(): installation qualification on a client computer or company server (see Installation Qualification of rpact)

Validation

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.

RPACT Connect

Connecting you to insights, downloads, and premium support: connect.rpact.com

About

rpact is a comprehensive validated¹ 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 (2025)

For more information please visit rpact.org

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

For more information please visit rpact.com

Friedrich Pahlke and Gernot Wassmer

2025-11-27