  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. This article proposes novel optimization criteria
for calibrating and evaluating candidate statistical designs of master
protocols in the presence of the potential for treatment effect heterogeneity
among enrolled patient subpopulations. The framework is applied to demonstrate
the statistical properties of conventional study designs when treatments offer
heterogeneous benefit as well as identify optimal designs devised to monitor
the potential for heterogeneity among patients with differing clinical
indications using Bayesian modeling.
