Free examples and use-cases:   rpact vignettes
rpact: Confirmatory Adaptive Clinical Trial Design and Analysis


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 Exploring the user interface of rpact

The online shiny app for rpact is available at The default settings when the Shiny app is loaded is for a fixed sample design, which means that there is only one look at the data (kMax = 1). In other words, the default setting is not for a sequential design, but a traditional design where the data is analyzed once. Moving the slider for the “Maximum number of stages” would increase the number of looks in the design (you can select up to up to 10 looks).

Screenshot of the default settings in the design tab of the rpact Shiny app.

Screenshot of the default settings in the design tab of the rpact Shiny app.

The rpact package focuses on Confirmatory Adaptive Clinical Trial Design and Analysis. In clinical trials, researchers mostly test directional predictions, and thus, the default setting is to perform a one-sided test. Outside of clinical trials, it might be less common to design studies testing a directional prediction, but it is often a good idea. In clinical trials, it is common to use a 0.025 significance level (or type I error rate) for one-sided tests, as it is deemed preferable in regulatory settings to set the type I error rate for one-sided tests at half the conventional type I error used in two-sided tests. In other fields, such as psychology, researchers typically use a 0.05 significance level, regardless of whether they perform a one-sided or two-sided test. A default 0.2 Type 2 error rate (or power of 0.8) is common in many fields, and the default setting for the Type II error rate.

Remember that you always need to justify your error rates – the defaults are most often not optimal choices in any real-life design (and it might be especially useful to choose a higher power, if possible).

2 Type I Error Rate Control in Sequential Designs

We can explore a group sequential design by moving the slider for the maximum number of stages to, say, kMax = 2. The option to choose a design appears above the slider in the form of three “Design” radio buttons (Group Sequential, Inverse Normal, and Fisher), which by default is set to a group sequential design – this is the type of designs we will focus on in this step by step tutorial. The other options are relevant for adaptive designs which we will not discuss here.

A new drop down menu has appeared below the box to choose a Type II error rate that asks you to specify the “Type of design”. This allows you to choose how you want to control the alpha level across looks. By default the choice is an O’Brien-Fleming design. Set the Type of Design to the Pocock (P) option. Note there is also a Pocock type alpha spending (asP) option – we will use that later.

Because most people in social sciences will probably have more experience with two-sided tests at an alpha of 0.05, choose a two-sided test and an alpha level of 0.05 choose those settings. The input window should now look like the example below: