🤖 AI Summary
This study addresses the lack of transparent, prespecified control in dynamically borrowing external information for clinical trials by proposing a standardized dynamic borrowing method. The approach updates weights via Bayes factors to integrate source data with a no-borrowing component, while imposing a uniform upper bound on these weights. This establishes an analytical relationship between effective sample size and heterogeneity penalties, enabling interpretable, data-adaptive constraints on borrowing. Grounded in Bayesian inference and exponential family model analysis, the method’s validity is demonstrated under normal and binomial settings. Furthermore, it achieves joint optimization of sample size and weight selection subject to operating characteristic constraints, offering a rigorous framework for evidence synthesis in clinical research.
📝 Abstract
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.