This R Markdown document provides an example for analysing trials with a continuous endpoint and sample size reassessment using rpact.

**First, load the rpact package**

```
library(rpact)
packageVersion("rpact") # version should be version 2.0.5 or later
```

`## [1] '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)`