Zhong, YujieCook, Richard J.2020-02-142020-02-142019-04-01https://doi.org/10.1007/s10985-018-9430-yhttp://hdl.handle.net/10012/15643This is the peer reviewed version of the following article: Yujie Zhong and Richard J. Cook, The effect of omitted covariates in marginal and partially conditional recurrent event analyses. Lifetime Data Analysis (2019), 25(2): 280-300 which has been published in final form at https://doi.org/10.1007/s10985-018-9430-y.There have been many advances in statistical methodology for the analysis of recurrent event data in recent years. Multiplicative semiparametric rate-based models are widely used in clinical trials, as are more general partially conditional rate-based models involving event-based stratification. The partially conditional model provides protection against extra-Poisson variation as well as event-dependent censoring, but conditioning on outcomes post-randomization can induce confounding and compromise causal inference. The purpose of this article is to examine the consequences of model misspecification in semiparametric marginal and partially conditional rate-based analysis through omission of prognostic variables. We do so using estimating function theory and empirical studies.enasymptotic biasconfoundingmarginalpartially conditionalrate functionrecurrent eventsArticle