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In early phase oncology drug development, single arm proof-of-concept (POC) studies are increasingly being used to drive the early decisions for future development of the drug. Decision-makings based on such studies, typically involving small sample size and early surrogate efficacy endpoints, are extremely challenging. In particular, given the tremendous competition in the development of immunotherapies, expedition of the most promising programs is desired. To this end, we have proposed a Bayesian three-tier approach to facilitate the decision-making process, inheriting all the benefits of Bayesian decision-making approaches and formally allowing the option of acceleration. With pre-specified Bayesian decision criteria, three types of decisions regarding the future development of the drug can be made: (1) terminating the program, (2) further investigation, considering totality of evidence or additional POC studies, and (3) accelerating the program. We further proposed a Bayesian adaptive three-tier (BAT) design, extending the decision-making approach to incorporate adaptive thresholds and allow for continuous monitoring of the study. We compare the performance of the proposed methods with some other existing methods through simulations.

A Bayesian three-tier quantitative decision-making framework for single arm studies in early phase oncology