title: Group sequential designs with prospectively planned rules for subpopulation enrichment.
abstract: We propose a class of randomized trial designs aimed at gaining the advantages of wider generalizability and faster recruitment while mitigating the risks of including a population for which there is greater a priori uncertainty. We focus on testing null hypotheses for the overall population and a predefined subpopulation. Our designs have preplanned rules for modifying enrollment criteria based on data accrued at interim analyses. For example, enrollment can be restricted if the participants from a predefined subpopulation are not benefiting from the new treatment. Our designs have the following features: the multiple testing procedure fully leverages the correlation among statistics for different populations; the asymptotic familywise Type I error rate is strongly controlled; for outcomes that are binary or normally distributed, the decision rule and multiple testing procedure are functions of the data only through minimal sufficient statistics. Our designs incorporate standard group sequential boundaries for each population of interest; this may be helpful in communicating the designs, because many clinical investigators are familiar with such boundaries, which can be summarized succinctly in a single table or graph. We demonstrate these designs through simulations of a Phase III trial of a new treatment for stroke. User-friendly, free software implementing these designs is described. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, U.S.A.
authors.forename: Michael
authors.initials: M
authors.lastname: Rosenblum
authors.affiliation: Division of Biostatistics and Bioinformatics, Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, U.S.A.
authors.forename: Brandon
authors.initials: B
authors.lastname: Luber
authors.affiliation: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, U.S.A.
authors.forename: Richard E
authors.initials: RE
authors.lastname: Thompson
authors.affiliation: Division of Biostatistics and Bioinformatics, Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, U.S.A.
authors.forename: Daniel
authors.initials: D
authors.lastname: Hanley
mesh_terms: D006801:Humans; D016032:Randomized Controlled Trials as Topic; D012107:Research Design; D018401:Sample Size; D012984:Software; D035501:Uncertainty
keywords: adaptive enrichment design; group sequential design; optimal sample size