DIA BSWG KOL Lecture Series, L20

Event Date:



Brian P. Hobbs, PhD (The University of Texas)
Alexander M. Kaizer, PhD (University of Colorado)
Emily Zabor, DrPH (Cleveland Clinic)

Title: Statistical considerations for trials that study multiple indications

Abstract: 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 and/or histologies. Consequently, characterization of subpopulation heterogeneity has become central to the formulation and selection of a study design. However, this transition to master protocols has led to challenges in identifying the optimal trial design and proper calibration of hyperparameters. We often evaluate a range of null and alternative scenarios, however there has been little guidance on how to synthesize the potentially disparate recommendations for what may be optimal. This may lead to the selection of suboptimal designs and statistical methods that do not fully accommodate the subpopulation heterogeneity. In this talk we present an overview of agnostic drug development, the potential benefit of sharing of information amongst baskets using multi-source exchangeability modeling (MEMs), considerations for calibrating hyperparameters and selection of methods to control the type I error rate, and a case study with the basket package in R for implementation.

Slides will be stored in http://www.bayesianscientific.org/kol-lecture-series/
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