Concordance-based prior to dynamically borrow information for pediatric extrapolation

Music City Center 

In pediatric drug development, effectively borrowing information from adult trials can significantly reduce sample size and improve trial efficiency while maintaining robust inference. We propose a novel Bayesian dynamic borrowing approach that adjusts the amount of information borrowed from adult populations based on the concordance of a clinical endpoint and a predictive biomarker between the two populations. Our method leverages both clinical and biomarker data to guide borrowing decisions, balancing the concordance and divergence observed in different endpoints. Through simulation studies and real clinical data examples, we demonstrate that our approach consistently improves estimation accuracy and power while maintaining appropriate type I error. The proposed framework has broad applications in regulatory settings where adaptive borrowing strategies are crucial for ethical and efficiency reasons.

Bayesian data borrowing

Pediatric extrapolation

Clinical trial 

Biopharmaceutical Section