On regional treatment effect assessment using robust MAP priors
使用稳健 MAP 先验评估区域处理效应AI 解读 ↓
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使用稳健 MAP 先验评估区域处理效应
- 研究问题
- 如何在评估区域与总体处理效应的一致性时动态借用外部信息，兼顾相似数据下的精度与差异数据下的偏差，并降低先验设计成本？
- 方法与思路
- 作者利用稳健元分析预测先验与幂先验的关系，构造后验分布的闭式近似，以便选择满足目标操作特性的先验参数。
- 材料陈述的贡献
- 摘要称近似方法显著减轻计算负担，并从贝叶斯假设检验角度解释 MAP 信息成分相较幂先验的吸引力，以实例展示区域一致性设计。
- 阅读关注点
- 应核对闭式近似的误差、混合先验和借用强度的设定，以及区域与外部数据冲突时的偏差、错误率和精度表现及比较标准。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实。摘要未说明数据模型、近似适用范围或操作特性数值，先验优势不能直接推广。
原文摘要
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.