The use of external data in clinical trials offers numerous advantages, such as reducing the number of patients, increasing study power, and shortening trial durations. In Bayesian inference, information in external data can be transferred into an informative prior for future borrowing (i.e., prior synthesis). However, multisource external data often exhibits heterogeneity, which can lead to information distortion during the prior synthesis. Clustering helps identifying the heterogeneity, enhancing the congruence between synthesized prior and external data, thereby preventing information distortion. Obtaining optimal clustering is challenging due to the trade-off between congruence with external data and robustness to future data. We introduce two overlapping indices: the overlapping clustering index (OCI) and the overlapping evidence index (OEI). Using these indices alongside a K-Means algorithm, the optimal clustering of external data can be identified by balancing the trade-off. Based on the clustering result, we propose a prior synthesis framework to effectively borrow information from multisource external data. By incorporating the (robust) meta-analytic predictive prior into this framework, we develop (robust) Bayesian clustering MAP priors. Simulation studies and real-data analysis demonstrate their superiority over commonly used priors in the presence of heterogeneity. Since the Bayesian clustering priors are constructed without needing data from the prospective study to be conducted, they can be applied to both study design and data analysis in clinical trials or experiments.

翻译：在临床试验中使用外部数据具有诸多优势，例如减少患者数量、提高研究效能以及缩短试验周期。在贝叶斯推断中，外部数据中的信息可通过构建信息性先验实现未来数据借用（即先验合成）。然而，多源外部数据常呈现异质性，可能导致先验合成过程中的信息失真。聚类分析有助于识别异质性，增强合成先验与外部数据之间的一致性，从而防止信息失真。由于需要权衡与外部数据的一致性和对未来数据的稳健性，获得最优聚类具有挑战性。本文引入两个重叠指数：重叠聚类指数（OCI）与重叠证据指数（OEI）。结合K-Means算法，通过平衡上述权衡关系可识别外部数据的最优聚类。基于聚类结果，我们提出一个先验合成框架以有效借用多源外部数据的信息。通过将（稳健）荟萃分析预测先验纳入该框架，我们构建了（稳健）贝叶斯聚类MAP先验。模拟研究与实际数据分析表明，在存在异质性的情况下，该方法优于常用先验。由于贝叶斯聚类先验的构建无需依赖即将开展的预期研究数据，该方法可同时应用于临床试验或实验的研究设计与数据分析阶段。