First, load the rpact package
library(rpact) packageVersion("rpact") # version should be version 2.0.5 or later
##  '3.3.2'
In this vigentte, we want to illustrate a design where at interim stages we are able to perform data-driven sample size adaptations. For this purpose, we use the inverse normal combination test for combining the \(p\)-values from the stages of the trial. This type of design ensures that the Type I error rate is controlled.
We want to use a three stage design with O`Brien and Fleming boundaries and additionally want to consider futility bounds -0.5 and 0.5 for the test statistics at the first and the second stage, respectively. Accordingly,
# Example of an inverse normal combination test: <- getDesignInverseNormal(futilityBounds = c(-0.5, 0.5))designIN
defines the design to be used for this purpose. By default, this is a design with equally spaced information rates and one sided \(\alpha = 0.025\). The critical values can be displayed on the \(z\)-value or the \(p\)-value scale:
plot(designIN, type = 1)