Design and analysis of clinical trials in the presence of delayed treatment effect. (2nd February 2016)

• Record Type: Journal Article
• Authors: Sit, Tony; Liu, Mengling; Shnaidman, Michael; Ying, Zhiliang
• Is Part Of: Statistics in medicine. Volume 35:Number 11(2016)
• Page Start: 1774
• Page End: 1779
• Publication Date: 2016-02-02
• Subjects: lagged treatment effect -- log‐rank test -- power calculation; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.6889
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : In clinical trials with survival endpoint, it is common to observe an overlap between two Kaplan–Meier curves of treatment and control groups during the early stage of the trials, indicating a potential delayed treatment effect. Formulas have been derived for the asymptotic power of the log‐rank test in the presence of delayed treatment effect and its accompanying sample size calculation. In this paper, we first reformulate the alternative hypothesis with the delayed treatment effect in a rescaled time domain, which can yield a simplified sample size formula for the log‐rank test in this context. We further propose an intersection‐union test to examine the efficacy of treatment with delayed effect and show it to be more powerful than the log‐rank test. Simulation studies are conducted to demonstrate the proposed methods. Copyright © 2016 John Wiley & Sons, Ltd.