Vignettes

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
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).

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.

Multi-arm
16 Simulation of a Trial with a Binary Endpoint and Unblinded Sample Size Re-Calculation with rpact Planning Categorical

This R Markdown document provides examples for assessing trials with adaptive sample size re-calculation (SSR) using rpact. It also shows how to implement the promizing zone approach as proposed by Mehta and Pocock 2011 and further developed by Hsiao et al 2019 with rpact.

Power simulation
3 Designing Group Sequential Trials with a Binary Endpoint with rpact Planning Categorical

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

14 Planning a Trial with Binary Endpoints with rpact Planning Categorical

This R Markdown document provides an example for planning a trial with a binary endpoint using rpact. It also illustrates the use of ggplot2 for illustrating the characteristics of a sample size recalculation strategy. Another example for planning a trial with binary endpoints can be found in the vignette Designing group sequential trials with a binary endpoint with rpact.

Sample size, Power simulation
4 Designing Group Sequential Trials with Two Groups and a Survival Endpoint with rpact Planning Survival

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

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

Events

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Testimonials

Daniel

TU/e

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

Director

Pharma

“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…

Daniel

TU/e

“[…] 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…