Multiplicity for a group sequential trial with biomarker subpopulations

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 benefit-risk profile and improving trial power. Studying a broad population simultaneously with one allows determine which treatment is goal making approved regulatory labeling as inclusive appropriate. We examine new methods accounting complete correlation structure group sequential designs hypotheses nested subgroups. provide full control family-wise Type I error rate. This extension previous either design or between subgroups improves efficiency (power sample size) over typical Bonferroni approach testing populations.