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All Times EDT

Friday, September 25
Fri, Sep 25, 11:45 AM - 12:45 PM
Virtual
Poster Session

PS31-Analysis Methods for Vaccine Efficacy in a Fixed Duration Design with Censoring (301143)

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Frank Liu, Merck & Co. 
Radha A. Railkar, Biostatistics and Research Decision Sciences, Merck & Co., Inc., North Wales, PA, USA 
*Ying Zhang, Biostatistics and Research Decision Sciences, Merck & Co., Inc., North Wales, PA, USA 

Keywords: vaccine efficacy, censoring, Poisson regression, controlled-based imputation

A fixed duration trial may be considered to evaluate vaccine efficacy to show the vaccine reduces the incidence of the disease of interest compared to placebo within a given duration of follow-up. Conventionally, the vaccine efficacy is defined as the relative risk reduction 1-(Rv/Rp), with Rv and Rp as the incidence rates for the disease of interest in the vaccine and placebo groups, respectively. A conditional exact method proposed by Chan and Bohidar (1998) is often used to estimate the vaccine efficacy and its confidence interval. In this study, we firstly compared the conditional exact method with two alternative approaches including Poisson regression and modified Poisson regression proposed by Zou (2004) through simulations. In practice, to account for censoring due to early dropout, a sensitivity analysis could be implemented through controlled-based imputation (CBI) methods. As a second part in this study, we compared three CBI methods through simulations, including 1) analytic approach; 2) multiple imputation; and 3) multiple imputation + bootstrap. The simulation study has been conducted to compare these methods under different sample size, dropout rate, and incidence rate assumptions. The statistical properties and performance for these methods in terms of power and type I error will be evaluated.