Statistical design considerations for trials that study multiple indications

Alexander M. Kaizer, Joseph S. Koopmeiners, Nan Chen, Brian P. Hobbs

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)785-798
Number of pages14
JournalStatistical methods in medical research
Volume30
Issue number3
DOIs
StatePublished - Mar 2021

Bibliographical note

Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institute on Drug Abuse [grant no. NIH R01-DA046320]; and the Case Comprehensive Cancer Center [grant no. P30 CA043703].

Publisher Copyright:
© The Author(s) 2020.

Keywords

  • Adaptive design
  • Bayesian analysis
  • hyperparameter calibration
  • master protocols
  • multiple comparisons

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