Work

A group‐sequential randomized trial design utilizing supplemental trial data

Year: 2021

Type: article

Abstract: 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.

Source: Statistics in Medicine

DOI: 10.1002/sim.9249

Authors: Ales Kotalik, David M. Vock, Brian P. Hobbs, Joseph S. Koopmeiners

Institutions: AstraZeneca (United States), University of Minnesota, The University of Texas at Austin

Language: English

Topic: Statistical Methods in Clinical Trials

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Heart, Lung, and Blood Institute, National Institute on Drug Abuse, National Cancer Institute