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.

Bayesian hierarchical regression (BHR) is often used in small area estimation (SAE). BHR conditions on the samples. Therefore, when data are from a complex sample survey, neither survey sampling design nor survey weights are used. This can introduce bias and/or cause large variance. Further, if non-informative priors are used, BHR often requires the combination of multiple years of data to produce sample sizes that yield adequate precision; this can result in poor timeliness and can obscure trends. To address bias and variance, we propose a design assisted model-based approach for SAE by integrating adjusted sample weights. To address timeliness, we use historical data to define informative priors (power prior); this allows estimates to be derived from a single year of data. Using American Community Survey data for validation, we applied the proposed method to Behavioral Risk Factor Surveillance System data. We estimated the prevalence of disability for all U.S. counties. We show that our method can produce estimates that are both more timely than those arising from widely-used alternatives and are closer to ACS’ direct estimates, particularly for low-data counties. Our method can be generalized to estimate the county-level prevalence of other health related measurements.