Discussion and conclusion
Explicit control of FWER provides more reliable evidence of efficacy in the final analysis, even in basket trials, and improves decision-making for regulatory drug approvals. Existing Bayesian methods often control the empirical FWER using simulation-based procedures, but the FWER is not controlled when some assumptions are violated. In this study, we provided closed-form equations for the type I and II error rates and FWER in the context of the proposed Bayesian two-stage design and developed a numerical approach for determining the thresholds for controlling the well-approximated FWER to the exact value. To achieve more accurate control of the well-approximated FWER, the proposed method can also be used to adjust the threshold of This study showed that the proposed method could weakly control the FWER. However, from a regulatory perspective, strong control of the FWER is necessary, especially in confirmatory basket trials. Therefore, methodological studies of theoretical control of FWER at a target level are required.In the Bayesian context, controlling the exact FWER is less fundamental when evaluating the therapeutic effect but should be well-calibrated at the planning stage of trials [We estimated the posterior probability using the independent and BHM methods in this study; however, other Bayesian dynamic borrowing information models for estimating the posterior probability, such as the extended BHM, the exchangeability-non exchangeability (EXNEX) approach, or Bayesian model averaging can also be used [