param_normalApproximation

Parameter Description: Normal Approximation

internal

Design and analysis of confirmatory adaptive clinical trials with continuous, binary, and survival endpoints according to the methods described in the monograph by Wassmer and Brannath (2016) <doi:10.1007/978-3-319-32562-0>. This includes classical group sequential as well as multi-stage adaptive hypotheses tests that are based on the combination testing principle.

Friedrich Pahlke

rpact

Confirmatory Adaptive Clinical Trial Design and Analysis

Gernot Wassmer

Till Jensen

Daniel Sabanes Bove

Stephen Schueuerhuis

Tobias Muetze

param_normalApproximation function

<dl><dt>normalApproximation</dt>
<dd>The type of computation of the p-values. Default is <code>FALSE</code> for
testing means (i.e., the t test is used) and <code>TRUE</code> for testing rates and the hazard ratio.
For testing rates, if <code>normalApproximation = FALSE</code> is specified, the binomial test
(one sample) or the exact test of Fisher (two samples) is used for calculating the p-values.
In the survival setting <code>normalApproximation = FALSE</code> has no effect.</dd></dl>

Arguments

Parameter Description: Normal Approximation — param_normalApproximation

<dl>

<dt>normalApproximation</dt>
<dd>The type of computation of the p-values. Default is <code>FALSE</code> for
testing means (i.e., the t test is used) and <code>TRUE</code> for testing rates and the hazard ratio.
For testing rates, if <code>normalApproximation = FALSE</code> is specified, the binomial test
(one sample) or the exact test of Fisher (two samples) is used for calculating the p-values.
In the survival setting <code>normalApproximation = FALSE</code> has no effect.</dd>

</dl>