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Activity Number: 31 - Personalized/Precision Medicine II
Type: Contributed
Date/Time: Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #301836 Presentation
Title: Statistical Considerations for Trials That Study Multiple Indications
Author(s): Alexander Kaizer* and Joseph Koopmeiners and Nan Chen and Brian Hobbs
Companies: University of Colorado Anschutz Medical Campus and University of Minnesota and University of Texas M.D. Anderson Cancer Center and Taussig Cancer Institute, Cleveland Clinic
Keywords: Master protocol; oncology; multiple comparisons; Bayesian hierarchical model; Basket design

Breakthroughs in cancer biology have defined new research programs emphasizing the development of therapies that target specific pathways in tumor cells. Innovations in clinical trial design have followed with master protocols defined by inclusive eligibility criteria and evaluations of multiple therapies or histologies. Contemporary trials are being devised to ascertain the extent to which a therapy offers benefit to patient subpopulations defined by a common target. Consequently, characterization of subpopulation heterogeneity has become central to study design. Yet, trial formulation continues to rely on approaches to study design that evaluate the hypothesis of “average benefit.” Often these designs fail to acknowledge heterogeneity or assume that treatment effects are statistically independent among subtypes. We propose optimization criteria for evaluating candidate designs of master protocols with the potential for heterogeneity among subpopulations. We demonstrate the statistical properties of the framework for conventional designs with heterogeneity as well as identify optimal designs that monitor the potential for heterogeneity among patients using Bayesian modeling.

Authors who are presenting talks have a * after their name.

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