A Bayesian basket trial design that borrows information across strata based on the similarity between the posterior distributions of the response probability. Issue 2 (14th October 2019)

• Record Type: Journal Article
• Authors: Fujikawa, Kei; Teramukai, Satoshi; Yokota, Isao; Daimon, Takashi
• Is Part Of: Biometrical journal. Volume 62:Issue 2(2020:Mar.)
• Page Start: 330
• Page End: 338
• Publication Date: 2019-10-14
• Subjects: basket trial -- Bayesian predictive sample size determination -- clinical trial design -- interim analysis -- Jensen–Shannon divergence; Biometry -- Periodicals; Medical statistics -- Periodicals; 570.15195
• DOI: 10.1002/bimj.201800404
• Languages: English
• ISSNs: 0323-3847

Abstract

Abstract: Basket trials simultaneously evaluate the effect of one or more drugs on a defined biomarker, genetic alteration, or molecular target in a variety of disease subtypes, often called strata. A conventional approach for analyzing such trials is an independent analysis of each of the strata. This analysis is inefficient as it lacks the power to detect the effect of drugs in each stratum. To address these issues, various designs for basket trials have been proposed, centering on designs using Bayesian hierarchical models. In this article, we propose a novel Bayesian basket trial design that incorporates predictive sample size determination, early termination for inefficacy and efficacy, and the borrowing of information across strata. The borrowing of information is based on the similarity between the posterior distributions of the response probability. In general, Bayesian hierarchical models have many distributional assumptions along with multiple parameters. By contrast, our method has prior distributions for response probability and two parameters for similarity of distributions. The proposed design is easier to implement and less computationally demanding than other Bayesian basket designs. Through a simulation with various scenarios, our proposed design is compared with other designs including one that does not borrow information and one that uses a Bayesian hierarchical model.