Enhancement of Basket Trial Designs with Incorporation of a Bayesian Three-Outcome Decision-Making Framework

Abstract

With recent accelerated approvals of histology-agnostic novel agents, conducting basket trials that evaluate an investigational therapy on different histologies is gaining momentum, thanks to the underlying common biological anti-cancer mechanism of action. Statistical models are proposed to boost statistical efficiency by leveraging information across cohorts to harvest the shared response signal. However, limited research exists about establishing a quantitative decision-making framework for basket trials. Robustness of a dichotomized “Go/No-Go” decision may be suboptimal when an “inconclusive” decision is more appropriate. Accordingly, the three-outcome decision-making (3ODM) framework with an additional “Consider” zone has gained popularity, and we propose to incorporate 3ODM into basket trials for the benefit of robustness and flexibility, and for formal incorporation of between-cohort shared signal into 3ODM to improve its performance in basket trials. Our simulation study is the first to compare modeling of log odds ratio (on top of benchmark response rates, RRs) with modeling of RRs in most current basket trial designs, considering the potential drastic differences for reference and target RRs across cohorts. We used the exchangeability-nonexchangeability (EXNEX) model to evaluate operating characteristics of the EXNEX + 3ODM framework (with/without an interim analysis), although the proposed enhancement could be readily extended to different basket trial designs.

Keywords:

• Basket trials
• Bayesian three-outcome decision-making
• Oncology
• Proof-of-concept studies

Supplementary Materials

In the supplementary material, we first provide additional simulation results to complete the comparisons between modelling RR and modeling LOR (either under “2ODM” or under “3ODM and IA”). Second, for better illustration, we include the stacked bar charts for all simulation results.

Acknowledgments

The authors would like to thank the Editor-in-Chief and two anonymous referees for their times in reviewing the manuscript and providing insightful comments, and Gautier Paux (Sanofi) and Hui Quan (Sanofi) for the helpful discussions on the revised manuscript. Preliminary results of this manuscript were disclosed as an oral presentation (parallel session) at the 2022 ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop in Maryland (September 20–22, 2022). The authors would like to thank the session discussant Daniel Rubin (FDA/CDER) for his helpful comments.

Disclosure Statement

All authors declare that they have no conflict of interest.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.