Multiplicity for a Group Sequential Trial with Biomarker Subpopulations

TL;DR

This paper develops new group sequential trial methods that account for correlations in biomarker subpopulations, improving efficiency and error control in targeted therapy studies.

Contribution

It introduces methods that fully incorporate correlation structures in nested subgroup hypotheses, enhancing trial efficiency over traditional approaches.

Findings

Full control of family-wise Type I error rate achieved.

Improved power or reduced sample size compared to Bonferroni methods.

Applicable to drug development with biomarker subpopulations.

Abstract

Biomarker subpopulations have become increasingly important for drug development in targeted therapies. The use of biomarkers has the potential to facilitate more effective outcomes by guiding patient selection appropriately, thus enhancing the benefit-risk profile and improving trial power. Studying a broad population simultaneously with a more targeted one allows the trial to determine the population for which a treatment is effective and allows a goal of making approved regulatory labeling as inclusive as is appropriate. We examine new methods accounting for the complete correlation structure in group sequential designs with hypotheses in nested subgroups. The designs provide full control of family-wise Type I error rate. This extension of previous methods accounting for either group sequential design or correlation between subgroups improves efficiency (power or sample size) over a typical Bonferroni approach for testing nested populations.