Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.
Stat Med. 2022 Sep 30;41(22):4367-4384. doi: 10.1002/sim.9514. Epub 2022 Jul 1.
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.
我们提出了一种基于局部多源可交换性假设（疾病类型之间）的信息借用策略，用于设计和监测 II 期篮子试验。在我们提出的局部-MEM 框架中，信息借用仅允许在局部进行，即在具有相似反应率的篮子之间进行，并且信息借用的数量由反应率的相似性水平决定，而不考虑相似性的篮子不允许共享信息。我们使用所提出的策略为 II 期篮子试验构建了两阶段设计。在所提出的方法与竞争的贝叶斯方法和 Simon 的两阶段设计在各种模拟场景下进行了比较。我们证明，与 Simon 的两阶段设计相比，所提出的方法能够在合理的水平上保持总体 I 型错误率，并且具有理想的篮子效能。此外，与现有的贝叶斯方法相比，我们的方法在计算上效率更高，因为可以显式地推导出感兴趣的后验分布，而无需采样算法。在 https://github.com/yilinyl/Bayesian-localMEM 上提供了实现所提出的方法的 R 脚本。
