Definitive clinical trials are resource intensive, often requiring a large number of participants over several years. One approach to improve the efficiency of clinical trials is to incorporate historical information into the primary trial analysis. This approach has tremendous potential in the areas of pediatric or rare disease trials, where achieving reasonable power is difficult. In this article, we introduce a novel Bayesian group-sequential trial design based on Multisource Exchangeability Models, which allows for dynamic borrowing of historical information at the interim analyses. Our approach achieves synergy between group sequential and adaptive borrowing methodology to attain improved power and reduced sample size. We explore the frequentist operating characteristics of our design through simulation and compare our method to a traditional group-sequential design. Our method achieves earlier stopping of the primary study while increasing power under the alternative hypothesis but has a potential for type I error inflation under some null scenarios. We discuss the issues of decision boundary determination, power and sample size calculations, and the issue of information accrual. We present our method for a continuous and binary outcome, as well as in a linear regression setting.
Bibliographical noteFunding Information:
This research was partially funded by NIH under grants T32‐HL129956 from the National Heart, Lung, and Blood Institute, P30‐CA077598, R01‐CA214824, and R01‐CA225190 from the National Cancer Institute, and R03‐DA041870, R01‐DA046320 and U54‐DA031659 from the National Institute on Drug Abuse and FDA Center for Tobacco Products (CTP). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or Food and Drug Administration Center for Tobacco Products.
© 2021 John Wiley & Sons Ltd.
PubMed: MeSH publication types
- Journal Article
- Randomized Controlled Trial
- Research Support, N.I.H., Extramural