Heterogeneity in efficacy is sometimes observed across baskets in basket trials. In this study, we propose a model-free clustering framework that groups baskets based on transition probabilities derived from the trajectories of treatment response, rather than relying solely on a single efficacy endpoint such as the objective response rate. The number of clusters is not predetermined but is automatically determined in a data-driven manner based on the similarity structure among baskets. After clustering, baskets within the same cluster are analyzed using a hierarchical Bayesian model. This framework aims to improve the estimation precision of efficacy endpoints and enhance statistical power while maintaining the type~I error rate at the nominal level. The performance of the proposed method was evaluated through simulation studies. The results demonstrated that the proposed method can accurately identify cluster structures in heterogeneous settings and, even under such conditions, maintain the type~I error rate at the nominal level while improving statistical power.

翻译：篮式试验中有时观察到不同篮组之间存在疗效异质性。本研究提出一种无模型聚类框架，该框架基于治疗反应轨迹导出的转移概率对各篮组进行聚类，而非仅依赖单一疗效终点（如客观缓解率）。聚类数量并非预先设定，而是根据篮组间的相似性结构以数据驱动方式自动确定。聚类后，同一聚类内的篮组采用分层贝叶斯模型进行分析。该框架旨在改善疗效终点的估计精度，并在将I类错误率控制在名义水平的同时提升统计效能。通过模拟研究评估了所提方法的性能。结果表明，所提方法能准确识别异质性设置中的簇结构，且在此类条件下仍能将I类错误率维持在名义水平，同时提升统计效能。