  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.
