Adjusted inference for multiple testing procedure in group sequential designs

TL;DR

This paper introduces a novel method for adjusting p-values in group sequential trials to control the family-wise error rate when testing multiple hypotheses over multiple analyses.

Contribution

It proposes adjusted-sequential p-values that account for both multiple testing and multiple analyses, filling a gap in existing statistical methods.

Findings

Adjusted-sequential p-values effectively control FWER in simulations.

Application with weighted Bonferroni tests demonstrates practical utility.

Method improves inference accuracy in complex sequential testing scenarios.

Abstract

Adjustment of statistical significance levels for repeated analysis in group sequential trials has been understood for some time. Similarly, methods for adjustment accounting for testing multiple hypotheses are common. There is limited research on simultaneously adjusting for both multiple hypothesis testing and multiple analyses of one or more hypotheses. We address this gap by proposing adjusted-sequential p-values that reject an elementary hypothesis when its adjusted-sequential p-values are less than or equal to the family-wise Type I error rate (FWER) in a group sequential design. We also propose sequential p-values for intersection hypotheses as a tool to compute adjusted sequential p-values for elementary hypotheses. We demonstrate the application using weighted Bonferroni tests and weighted parametric tests, comparing adjusted sequential p-values to a desired FWER for inference on each elementary hypothesis tested.