Statistical design considerations for trials that study multiple indications

TL;DR

This paper proposes new optimization criteria for designing master clinical trials that account for heterogeneity among patient subpopulations, improving the evaluation of multiple therapies across different indications.

Contribution

It introduces a novel framework for calibrating and evaluating trial designs considering treatment heterogeneity, with Bayesian methods to optimize subpopulation monitoring.

Findings

Conventional designs may be suboptimal in heterogeneous settings

Bayesian models can effectively identify optimal trial designs

Framework improves decision-making in multi-indication trials

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. 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.