Bayesian local exchangeability design for phase II basket trials

TL;DR

This paper introduces a Bayesian local exchangeability approach for phase II basket trials that allows for targeted information sharing among similar baskets, improving efficiency and error control.

Contribution

It develops a novel local-MEM framework enabling selective information borrowing based on basket similarity, enhancing trial design and analysis.

Findings

Maintains family-wise type I error rate effectively.

Achieves higher basket-wise power than traditional methods.

Offers computational efficiency with explicit posterior profiles.

Abstract

We propose an information borrowing strategy for the design and monitoring of phase II basket trials based on the local multisource exchangeability assumption between baskets (disease types). In our proposed local-MEM framework, information borrowing is only allowed to occur locally, i.e., among baskets with similar response rate and the amount of information borrowing is determined by the level of similarity in response rate, whereas baskets not considered similar are not allowed to share information. We construct a two-stage design for phase II basket trials using the proposed strategy. The proposed method is compared to competing Bayesian methods and Simon's two-stage design in a variety of simulation scenarios. We demonstrate the proposed method is able to maintain the family-wise type I error rate at a reasonable level and has desirable basket-wise power compared to Simon's two-stage design. In addition, our method is computationally efficient compared to existing Bayesian methods in that the posterior profiles of interest can be derived explicitly without the need for sampling algorithms.