Developed for practical use: below you find a collection of practical examples and use-cases, the so-called rpact vignettes.

In addition to these public open access vignettes, our RPACT SLA customers have access to exclusive vignettes on special topics such as the analysis of multi-stage data with covariates from raw data.

# Title Category Sort descending Endpoint Summary
23 Planning and Analyzing a Group-Sequential Multi-Arm Multi-Stage Design with Binary Endpoint using rpact Getting started Categorical

This R Markdown document provides an example of implementing, simulating and analyzing multi-arm-multi-stage (MAMS) designs for testing rates with rpact with special regards to futility bound determination, treatment arm selection and generic data analysis.

After exemplarily using the binary endpoint analysis module from rpact, an illustrative landmark analysis (comparison of empirical survival probabilities at specific time point) using Greenwoods standard error estimation is to be performed. Since rpact itself does not directly support this type of analysis, another packages’ functionality needs to be utilized to perform the survival probability and standard error estimation to eventually use the estimates as input for a hypothetical continuous endpoint dataset which subsequently is to be analyzed as such.

22 Step-by-Step rpact Tutorial Getting started Categorical, Continuous, Survival

The R package rpact has been developed to design sequential and adaptive experiments. Many of the functions of the R package are available in an online Shiny app. For more information about rpact, including a quick start guide and manual, visit the rpact website. This step by step vignette accompanies the manuscript “Group Sequential Designs: A Tutorial” by Lakens, Pahlke, & Wassmer (2021).

1 Defining Group Sequential Boundaries with rpact Planning Categorical, Continuous, Survival

This R Markdown document provides example code for the the definition of the most commonly used group-sequential boundaries in rpact.

15 Planning a Survival Trial with rpact Planning Survival

This R Markdown document provides an example for planning a trial with a survival endpoint using rpact thereby illustrating the different ways of entering recruitment schemes. It also demonstrates the use of the survival simulation function.

Power simulation, Sample size
6 An Example to Illustrate Boundary Re-Calculations during the Trial with rpact Planning Survival

This R Markdown document provides an example for updating the group sequential boundaries when using an alpha-spending function approach based on observed information rates in rpact. Since version 3.1 of rpact, an additional option in the getAnalysisResults() function provides an easy way to perform an analysis with critical values that are calculated subsequently during the stages of the trial.

5 Simulation-Based Design of Group Sequential Trials with a Survival Endpoint with rpact Planning Survival

This R Markdown document describes how to simulate design characterics for survival design under complex settings (incl. non-proportional hazards) in rpact.

Power simulation
11 Comparing Sample Size and Power Calculation Results for a Group Sequential Trial with a Survival Endpoint: rpact vs. gsDesign Planning Survival

This R Markdown document provides an example that illustrates how to compare sample size and power calculation results of the two different R packages rpact and gsDesign.

2 Designing Group Sequential Trials with Two Groups and a Continuous Endpoint with rpact Planning Continuous

This R Markdown document provides examples for designing trials with continuous endpoints using rpact.

26 Delayed Response Designs with rpact Planning Categorical, Continuous, Survival

This R Markdown document provides a brief introduction to group sequential designs with delayed responses as proposed by Hampson and Jennison (2013). It is shown how this is implemented in rpact. Examples for designing trials with delayed responses using the software are provided. We also describe an alternative approach that directly uses the α-spending approach to derive the decision boundaries.

12 Supplementing and Enhancing rpact’s Graphical Capabilities with ggplot2 Planning Continuous

The aim of this R Markdown document is to give a brief description on how easy it is to supplement and enhance plots generated in rpact by use of the ggplot2 package and associated language.

Power simulation


Presentation for the U.S. Food and Drug Administration (FDA), March 3, 2022, 9:00am - 11:00am…

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Online Training Course for PPD, January 13, 2022.

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“rpact is by far the easiest to use.”
(Professor Daniel Lakens, Human-Technology Interaction Group, Eindhoven University of Technology, The Netherlands)



“We regularly use rpact for the design of group-sequential and adaptive trials at our company. The package is continuously evolving and includes state-of-the-art methods such as estimation of…



“[…] it is an incredibly accessible and useful tool for sequential analyses. […] I think your rpact package and shiny app might be a bit of a game-changer on this front, as it makes the required…