Bayesian dynamic borrowing has become an increasingly important tool for evaluating the consistency of regional treatment effects which is a key requirement for local regulatory approval of a new drug. It helps increase the precision of regional treatment effect estimate when regional and global data are similar, while guarding against potential bias when they differ. In practice, the two-component mixture prior, of which one mixture component utilizes the power prior to incorporate external data, is widely used. It allows convenient prior specification, analytical posterior computation, and fast evaluation of operating characteristics. Though the robust meta-analytical-predictive (MAP) prior is broadly used with multiple external data sources, it remains underutilized for regional treatment effect assessment (typically only one external data source is available) due to its inherit complexity in prior specification and posterior computation. In this article, we illustrate the applicability of the robust MAP prior in the regional treatment effect assessment by developing a closed-form approximation for its posterior distribution while leveraging its relationship with the power prior. The proposed methodology substantially reduces the computational burden of identifying prior parameters for desired operating characteristics. Moreover, we have demonstrated that the MAP prior is an attractive choice to construct the informative component of the mixture prior compared to the power prior. The advantage can be explained through a Bayesian hypothesis testing perspective. Using a real-world example, we illustrate how our proposed method enables efficient and transparent development of a Bayesian dynamic borrowing design to show regional consistency.

翻译：贝叶斯动态借用在评估区域治疗效果一致性方面已成为日益重要的工具，这是新药获得地区监管批准的关键要求。当区域数据与全球数据相似时，该方法有助于提高区域治疗效果估计的精度，同时在数据存在差异时防范潜在偏倚。实践中，双组分混合先验被广泛采用，其中一个混合分量利用幂先验来整合外部数据。该方法支持便捷的先验设定、解析后验计算以及操作特性的快速评估。尽管稳健元分析预测（MAP）先验在多外部数据源场景中得到广泛应用，但由于其在先验设定和后验计算方面固有的复杂性，在区域治疗效果评估（通常仅有一个外部数据源可用）中仍未得到充分利用。本文通过建立其后验分布的闭式近似解，并利用其与幂先验的关联关系，阐明了稳健MAP先验在区域治疗效果评估中的适用性。所提出的方法显著降低了为获得期望操作特性而识别先验参数的计算负担。此外，我们证明了相较于幂先验，MAP先验是构建混合先验信息分量的更优选择。这一优势可通过贝叶斯假设检验的视角进行解释。通过实际案例，我们展示了所提方法如何支持贝叶斯动态借用设计的高效透明开发，以证明区域一致性。