Bayesian dynamic borrowing methods incorporate historical control data into current clinical trial analyses while allowing the degree of borrowing to depend on the compatibility between historical and current data. Although many methods have been proposed, the degree of borrowing is often difficult to interpret, especially when multiple historical control sources are available. This scoping review focuses on posterior quantification of borrowing from multiple historical controls. We discuss overall borrowing summaries based on effective historical sample size, together with method-specific source-level summaries of borrowing, information contribution, or compatibility arising from power priors, unit information priors, multisource exchangeability models, Dirichlet process mixture models, and potential bias models. We distinguish posterior borrowing measures from quantities describing prior information allocation or source-specific conflict. Two case studies, one with a binary endpoint and one with a continuous endpoint, illustrate that methods with broadly similar posterior treatment effect estimates may differ in both the overall amount and source-specific pattern of borrowing. These examples show that large overall borrowing may reflect selective borrowing from compatible historical sources rather than uniform borrowing from all sources. We recommend reporting treatment effect estimates together with overall and source-specific borrowing summaries, when available, to improve transparency in posterior inference.

翻译：贝叶斯动态借用方法将历史对照数据纳入当前临床试验分析，同时允许借用程度取决于历史数据与当前数据的兼容性。尽管已有多种方法被提出，但借用程度往往难以解释，尤其是当存在多个历史对照来源时。本范围综述聚焦于多源历史对照数据中借用的后验量化问题。我们讨论了基于有效历史样本量的总体借用汇总指标，以及源于幂先验、单位信息先验、多源可交换性模型、狄利克雷过程混合模型和潜在偏倚模型的方法特有来源级借用、信息贡献或兼容性度量。我们将后验借用度量与描述先验信息分配或来源特异性冲突的量化指标加以区分。两个案例研究（一个二元终点，一个连续终点）表明，后验处理效应估计值大致相似的方法可能在借用总量和来源特异性模式上存在差异。这些例子显示，较大的总体借用可能反映的是对兼容历史来源的选择性借用，而非对所有来源的均匀借用。我们建议在报告处理效应估计值时，同时报告总体与来源级借用汇总（若适用），以提高后验推断的透明度。