calgspn

<code>calgspn()</code> calculates the group-sequential p-values for multiple hypotheses.

It is often challenging to strongly control the family-wise type-1 error rate in the group-sequential trials with multiple endpoints (hypotheses). The inflation of type-1 error rate comes from two sources (S1) repeated testing individual hypothesis and (S2) simultaneous testing multiple hypotheses. The 'MultiGroupSequential' package is intended to help researchers to tackle this challenge. The procedures provided include the sequential procedures described in Luo and Quan (2023) <doi:10.1080/19466315.2023.2191989> and the graphical procedure proposed by Maurer and Bretz (2013) <doi:10.1080/19466315.2013.807748>. Luo and Quan (2013) describes three procedures, and the functions to implement these procedures are (1) seqgspgx() implements a sequential graphical procedure based on the group-sequential p-values; (2) seqgsphh() implements a sequential Hochberg/Hommel procedure based on the group-sequential p-values; and (3) seqqvalhh() implements a sequential Hochberg/Hommel procedure based on the q-values. In addition, seqmbgx() implements the sequential graphical procedure described in Maurer and Bretz (2013).

Xiaodong Luo

MultiGroupSequential

Group-Sequential Procedures with Multiple Hypotheses

Hui Quan

Sanofi 

calgspn function

<dl><dt>xm</dt>
<dd>Matrix of test statistics for hypotheses (in row) and each
interim (in column).</dd>
<dt>alpham</dt>
<dd>Matrix of cumulative alpha levels for the test statistics <code>xm</code>.
Must have the same dimensions as <code>xm</code>. For each row, alpha levels must be</dd>
<dt>critm</dt>
<dd>Matrix of critical values for the test statistics in <code>xm</code>. It
should be computed beforehand. Must have the same dimensions as <code>xm</code>.</dd>
<dt>matrix.list</dt>
<dd>List of correlation matrices corresponding to each
hypothesis.</dd>
<dt>sided</dt>
<dd>Integer vector indicating the side of the test:<ul>
<li><code>-1</code>: Reject if test statistic is smaller than or equal to the critical value (one-sided)</li>
<li><code>1</code>: Reject if test statistic is greater or equal to the critical value (one-sided)</li>
<li><code>0</code>: Reject if the absolute value of the test statistic is greater than the critical value (two-sided)</li>
</ul></dd></dl>

Arguments

Author

Calculate group-sequential p-values for multiple hypotheses — calgspn

<dl>

<dt>xm</dt>
<dd>Matrix of test statistics for hypotheses (in row) and each
interim (in column).</dd>


<dt>alpham</dt>
<dd>Matrix of cumulative alpha levels for the test statistics <code>xm</code>.
Must have the same dimensions as <code>xm</code>. For each row, alpha levels must be</dd>


<dt>critm</dt>
<dd>Matrix of critical values for the test statistics in <code>xm</code>. It
should be computed beforehand. Must have the same dimensions as <code>xm</code>.</dd>


<dt>matrix.list</dt>
<dd>List of correlation matrices corresponding to each
hypothesis.</dd>


<dt>sided</dt>
<dd>Integer vector indicating the side of the test:<ul>
<li><code>-1</code>: Reject if test statistic is smaller than or equal to the critical value (one-sided)</li>
<li><code>1</code>: Reject if test statistic is greater or equal to the critical value (one-sided)</li>
<li><code>0</code>: Reject if the absolute value of the test statistic is greater than the critical value (two-sided)</li>
</ul></dd>

</dl>

calgspn: Calculate group-sequential p-values for multiple hypotheses

Description

Usage

Value

Arguments

Author

Examples