With the recent paradigm shift from cytotoxic drugs to new generation of target therapy and immuno-oncology therapy during oncology drug developments, patients with various cancer (sub)types may be eligible to participate in a basket trial if they have the same molecular target. Bayesian hierarchical modeling (BHM) are widely used in basket trial data analysis, where they adaptively borrow information among different cohorts (subtypes) rather than fully pool the data together or doing stratified analysis based on each cohort. Those approaches, however, may have the risk of over shrinkage estimation because of the invalidated exchangeable assumption. We propose a two-step procedure to find the balance between pooled and stratified analysis. In the first step, we treat it as a clustering problem by grouping cohorts into clusters that share the similar treatment effect. In the second step, we use shrinkage estimator from BHM to estimate treatment effects for cohorts within each cluster under exchangeable assumption. For clustering part, we adapt the mixture of finite mixtures (MFM) approach to have consistent estimate of the number of clusters. We investigate the performance of our proposed method in simulation studies and apply this method to Vemurafenib basket trial data analysis.

翻译：随着肿瘤药物研发中从细胞毒性药物向新一代靶向治疗和免疫肿瘤治疗的模式转变，具有相同分子靶点的不同癌症（亚）类型的患者可能有资格参与篮子试验。贝叶斯分层模型（BHM）在篮子试验数据分析中被广泛使用，它能够在不同队列（亚型）之间自适应地借用信息，而不是完全合并数据或基于每个队列进行分层分析。然而，由于无效的可交换性假设，这些方法可能存在过度收缩估计的风险。我们提出了一种两步法来寻找合并分析与分层分析之间的平衡。第一步，我们将其视为聚类问题，将具有相似治疗效果的队列分组为簇。第二步，我们在可交换性假设下，使用BHM的收缩估计量来估计每个簇内队列的治疗效果。在聚类部分，我们采用有限混合模型（MFM）方法，以获得对簇数量的一致估计。我们通过模拟研究考察了所提出方法的性能，并将其应用于维莫非尼篮子试验数据分析。