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Although increasingly used as a data resource for assembling cohorts,electronic health records (EHRs) pose many analytic challenges. In particular,a patient's health status influences when and what data are recorded,generating sampling bias in the collected data. In this paper, we considerrecurrent event analysis using EHR data. Conventional regression methods forevent risk analysis usually require the values of covariates to be observedthroughout the follow-up period. In EHR databases, time-dependent covariatesare intermittently measured during clinical visits, and the timing of thesevisits is informative in the sense that it depends on the disease course.Simple methods, such as the last-observation-carried-forward approach, can leadto biased estimation. On the other hand, complex joint models requireadditional assumptions on the covariate process and cannot be easily extendedto handle multiple longitudinal predictors. By incorporating sampling weightsderived from estimating the observation time process, we develop a novelestimation procedure based on inverse-rate-weighting and kernel-smoothing forthe semiparametric proportional rate model of recurrent events. The proposedmethods do not require model specifications for the covariate processes and caneasily handle multiple time-dependent covariates. Our methods are applied to akidney transplant study for illustration.

Recurrent Events Analysis With Data Collected at Informative Clinical Visits in Electronic Health Records