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.Multi-arm
|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
|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
|20||Simulating Multi-Arm Designs with a Continuous Endpoint using rpact||Planning||Continuous||
This R Markdown document provides examples for simulating multi-arm multi-stage (MAMS) designs for testing means with rpact.Power simulation, 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.
Presentation for the U.S. Food and Drug Administration (FDA), March 3, 2022, 9:00am - 11:00am…
Presentation for Lantheus, January 18, 2022.
“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…
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