Bayesian joint models for multi-regional clinical trials

• Published in: Biostatistics (Oxford, England) Vol. 25; no. 3; pp. 852 - 866
• Main Authors: Bean, Nathan W, Ibrahim, Joseph G, Psioda, Matthew A
• Format: Journal Article
• Language: English
• Published: England Oxford Publishing Limited (England) 01.07.2024; Oxford University Press
• Subjects: Bayes Theorem; Bayesian analysis; Biostatistics - methods; Clinical trials; Clinical Trials as Topic - methods; Drug development; Humans; Information processing; Mathematical models; Models, Statistical; Multicenter Studies as Topic - methods; Pharmaceutical industry; Regional development; Rejection rate; Statistical methods; Survival; Survival Analysis; LEADER trial; Bayesian model averaging; Joint models; Bayesian clinical trials; Multi-regional clinical trials; Laplace approximation
• DOI: 10.1093/biostatistics/kxad023
• ISSN: 1465-4644; 1468-4357

In recent years, multi-regional clinical trials (MRCTs) have increased in popularity in the pharmaceutical industry due to their ability to accelerate the global drug development process. To address potential challenges with MRCTs, the International Council for Harmonisation released the E17 guidance document which suggests the use of statistical methods that utilize information borrowing across regions if regional sample sizes are small. We develop an approach that allows for information borrowing via Bayesian model averaging in the context of a joint analysis of survival and longitudinal data from MRCTs. In this novel application of joint models to MRCTs, we use Laplace’s method to integrate over subject-specific random effects and to approximate posterior distributions for region-specific treatment effects on the time-to-event outcome. Through simulation studies, we demonstrate that the joint modeling approach can result in an increased rejection rate when testing the global treatment effect compared with methods that analyze survival data alone. We then apply the proposed approach to data from a cardiovascular outcomes MRCT.