发表机构
Division of Biometrics IX, FDA/CDER/OTS/OB, Maryland, USA（美国食品药品监督管理局药物评价与研究中心办公室生物统计第九处）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出标准化动态借用（SDB）方法，通过标准化贝叶斯因子统一控制信息借用，提供可解释的借用约束，并支持功效和I类错误校准。
AI 中文摘要
动态借用可以利用外部信息提高临床试验的效率，但现有方法可能无法对借用提供透明、预先指定的控制。我们引入了标准化动态借用（SDB），该方法将来源信息组件与不借用组件相结合，并使用以其在可接受目标数据上的上确界标准化的贝叶斯因子来更新它们的相对几率。不借用组件下的后验密度通过对目标数据似然进行归一化获得。预先指定的来源组件初始权重是其后验权重的尖锐统一上界，提供了可解释的借用约束。当来源模型和目标模型属于同一单参数典型指数族时，后验期望局部信息比有效样本量等于目标样本量加上借用的来源先验有效样本量减去非负异质性惩罚。对于正态分布的结果，借用取决于标准化的来源-目标冲突Z分数，并根据标准正态核递减。我们还联合选择初始权重和目标样本量，以满足预先指定的功效和I类错误要求。正态和二项示例展示了SDB如何将来源-目标兼容性转化为数据自适应借用，以及校准程序如何在操作特征约束下预先指定来源组件初始权重和目标样本量。这些结果为预先指定、控制和解释动态借用提供了一个透明的框架。
英文摘要
Dynamic borrowing can improve efficiency in clinical trials using external information, but existing methods may not offer transparent, prespecified control over borrowing. We introduce Standardized Dynamic Borrowing (SDB), which combines a source-informed component with a no-borrowing component and updates their relative odds using a Bayes factor standardized by its supremum over admissible target data. The posterior density under the no-borrowing component is obtained by normalizing the target-data likelihood. The prespecified source-component initial weight is a sharp uniform upper bound on its posterior weight, providing an interpretable borrowing constraint. When source and target models belong to the same one-parameter canonical exponential family, posterior expected local-information-ratio effective sample size equals target sample size plus borrowed source-prior effective sample size minus a nonnegative heterogeneity penalty. For normally distributed outcomes, borrowing depends on a standardized source-target conflict $Z$-score and decreases according to a standard normal kernel. We also jointly select the initial weight and target sample size to meet prespecified power and Type I error requirements. Normal and binomial illustrations demonstrate how SDB translates source-target compatibility into data-adaptive borrowing and how the calibration procedure prespecifies the source-component initial weight and target sample size under operating-characteristic constraints. These results provide a transparent framework for prespecifying, controlling, and interpreting dynamic borrowing.
Comments33 pages, 5 figures, 1 table
