gbounds

This function computes the critical boundaries and the error spent until each stage in group sequential trials

The trigger strategy is a general framework for a multistage statistical design with multiple hypotheses, allowing an adaptive selection of interim analyses. The selection of interim stages can be associated with some prespecified endpoints which serve as the trigger. This selection allows us to refine the critical boundaries in hypotheses testing procedures, and potentially increase the statistical power. This package includes several trial designs using the trigger strategy.
See Gou, J. (2023), "Trigger strategy in repeated tests on multiple hypotheses", Statistics in Biopharmaceutical Research, 15(1), 133-140, and Gou, J. (2022), "Sample size optimization and initial allocation of the significance levels in group sequential trials with multiple endpoints", Biometrical Journal, 64(2), 301-311.

Jiangtao Gou

triggerstrategy

Trigger Strategy in Clinical Trials

Fengqing (Zoe) Zhang

gbounds function

<dl><dt>t</dt>
<dd>a vector of information times</dd>
<dt>iuse</dt>
<dd>a number of the type of the error spending function, from -2, -1, 1, 2, 3, 4</dd>
<dt>alpha</dt>
<dd>a number of type I error rate</dd>
<dt>phi</dt>
<dd>a parameter for the power family or the HSD gamma family</dd></dl>

Arguments

Author

Critical boundary in group sequential trials — gbounds

<dl>

<dt>t</dt>
<dd>a vector of information times</dd>


<dt>iuse</dt>
<dd>a number of the type of the error spending function, from -2, -1, 1, 2, 3, 4</dd>


<dt>alpha</dt>
<dd>a number of type I error rate</dd>


<dt>phi</dt>
<dd>a parameter for the power family or the HSD gamma family</dd>

</dl>

gbounds: Critical boundary in group sequential trials

Description

Usage

Value

Arguments

Author

Details

Examples