Bayesian local exchangeability design for phase II basket trials. (1st July 2022)

• Record Type: Journal Article
• Authors: Liu, Yilin; Kane, Michael; Esserman, Denise; Blaha, Ondrej; Zelterman, Daniel; Wei, Wei
• Is Part Of: Statistics in medicine. Volume 41:Number 22(2022)
• Page Start: 4367
• Page End: 4384
• Publication Date: 2022-07-01
• Subjects: adaptive design -- Bayesian analysis -- clustering -- master protocol -- oncology; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.9514
• Languages: English
• ISSNs: 0277-6715

Abstract

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, that is, 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. R scripts to implement the proposed method are available at https://github.com/yilinyl/Bayesian‐localMEM .