Abstract

This paper reviews the development of Bayesian statistical methods and identifies the main barriers limiting their adoption by the U.S. Food and Drug Administration (FDA) in drug and biologics regulation. Advantages of the Bayesian framework are highlighted, contrasting their adoption with that of frequentist methods. A synthesis of key historical, regulatory, and industry trends describes advances in regulatory science. For stakeholders aiming to improve drug development efficiency without compromising scientific rigor, the careful use of Bayesian methods-anchored in systematic prior construction, scenariobased evaluation of operating characteristics (often via simulation), transparent documentation and reproducible computation with early regulatory engagement-offers an increasingly practical pathway for drug development and post-approval surveillance.

Keywords:Bayesian framework; Frequentist approach; Clinical trials; Regulatory challenges in drug development; Regulatory science