Journal of Data Science

Pediatric vaccine efficacy studies face enrollment challenges, often resulting in underpowered studies to confirm vaccine efficacy. To address the challenge, this paper proposes a novel Two-Tier Bayesian dynamic borrowing framework that selectively incorporates two heterogeneous sources of historical data of adult populations into pediatric studies. The method employs similarity metrics based on intermediate outcomes, such as immunological markers, to dynamically determine borrowing weights via overlap coefficients, ensuring that only comparable data are utilized. Power priors are constructed with weights reflecting this similarity, while a cap on the effective sample size helps maintain control of the maximum information to borrow. Simulation studies demonstrate that the proposed approach improves power, reduces estimation bias and maintains type I error control, thus offering a robust and practical strategy for pediatric vaccine study design.