title: Design and analysis of clinical trials in the presence of delayed treatment effect.
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.
authors.affiliation: Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China.
authors.forename: Tony
authors.initials: T
authors.lastname: Sit
authors.affiliation: Departments of Population Health and Environmental Medicine, New York University School of Medicine, New York, 10016, NY.
authors.forename: Mengling
authors.initials: M
authors.lastname: Liu
authors.affiliation: Pfizer Inc, New York, 10017, NY, U.S.A.
authors.forename: Michael
authors.initials: M
authors.lastname: Shnaidman
authors.affiliation: Department of Statistics, Columbia University, New York, 10027, NY, U.S.A.
authors.forename: Zhiliang
authors.initials: Z
authors.lastname: Ying
mesh_terms: D000465:Algorithms; D002986:Clinical Trials as Topic; D003198:Computer Simulation; D023381:Endpoint Determination; D015340:Epidemiologic Research Design; D006801:Humans; D015233:Models, Statistical; D016019:Survival Analysis; D013997:Time Factors; D016896:Treatment Outcome
keywords: lagged treatment effect; log-rank test; power calculation
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