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：篮子试验中，不同篮子间的疗效异质性时有发生。本研究提出一种无模型聚类框架，该框架依据治疗响应轨迹推导出的转移概率对篮子进行分组，而非仅依赖客观缓解率等单一疗效终点。聚类数量并非预先设定，而是基于篮子间的相似性结构以数据驱动方式自动确定。完成聚类后，同一簇内的篮子采用分层贝叶斯模型进行分析。该框架旨在提升疗效终点估计的精确性，并在将I类错误率控制在名义水平的同时增强统计功效。通过模拟研究对所提方法的性能进行评估，结果表明：该方法能够准确识别异质场景下的聚类结构，且在此类条件下仍可在保持I类错误率于名义水平的同时提升统计功效。