The win ratio (WR) statistic is increasingly used to evaluate treatment effects based on prioritized composite endpoints, yet existing Bayesian adaptive designs are not directly applicable because the WR is a summary statistic derived from pairwise comparisons and does not correspond to a unique data-generating mechanism. We propose a Bayesian model-assisted adaptive design for randomized phase II clinical trials based on the WR statistic, referred to as the BMW design. The proposed design uses the joint asymptotic distribution of WR test statistics across interim and final analyses to compute posterior probabilities without specifying the underlying outcome distribution. The BMW design allows flexible interim monitoring with early stopping for futility or superiority and is extended to jointly evaluate efficacy and toxicity using a graphical testing procedure that controls the family-wise error rate (FWER). Simulation studies demonstrate that the BMW design maintains valid type I error and FWER control, achieves power comparable to conventional methods, and substantially reduces expected sample size. An R Shiny application is provided to facilitate practical implementation.

翻译：胜率统计量在基于优先复合终点的治疗效果评估中日益普及，但由于胜率是从成对比较中推导出的汇总统计量，且不对应唯一的数据生成机制，现有贝叶斯自适应设计无法直接适用。本文提出一种基于胜率统计量的随机化Ⅱ期临床试验贝叶斯模型辅助自适应设计，简称BMW设计。该设计利用胜率检验统计量在期中与最终分析中的联合渐近分布计算后验概率，无需设定潜在结局分布。BMW设计支持灵活的期中监测（可因无效或优效性提前终止），并通过控制族系错误率的图形检验程序扩展至疗效与毒性的联合评估。模拟研究表明，BMW设计能维持有效的Ⅰ类错误与族系错误率控制，获得与传统方法相当的检验效能，并显著降低预期样本量。本文提供的R Shiny应用程序可促进实际应用。