Empirical bayes approach for dynamic bayesian borrowing for clinical trials in rare diseases

• Published in: Journal of pharmacokinetics and pharmacodynamics 2023-12, Vol.50 (6), p.495-499
• Main Author: Sebastien, Bernard
• Format: Article
• Language: English
• Subjects: Bayes Theorem; Bayesian analysis; Biochemistry; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Clinical trials; Computer Simulation; Humans; Normal distribution; Original Paper; Pharmacodynamics; Pharmacokinetics; Pharmacology/Toxicology; Pharmacy; Rare diseases; Rare Diseases - drug therapy; Research Design; Sample Size; Veterinary Medicine/Veterinary Science
• ISSN: 1567-567X; 1573-8744
• DOI: 10.1007/s10928-023-09860-0

Application of Bayesian methods is one the tools that can be used to face the multiple challenges that are met when clinical trials must be conducted in rare diseases. We propose in this work to use a dynamic Bayesian borrowing approach, based on a mixture prior, to complement the control arm of a comparative trial and estimate the mixture parameter by an Empirical Bayes approach. The method is compared, using simulations, with an approach based on a pre-specified (non-adaptive) informative prior. The simulation study shows that the proposed method exhibits similar power as the non-adaptive prior and drastically reduce type I error in case of severe discrepancy between the informative prior and the study control arm data. In case of limited discrepancy between the informative prior and the study control arm data, then our proposed adaptive prior does not reduce the inflation of the type I error.