  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 consider
recurrent event analysis using EHR data. Conventional regression methods for
event risk analysis usually require the values of covariates to be observed
throughout the follow-up period. In EHR databases, time-dependent covariates
are intermittently measured during clinical visits, and the timing of these
visits is informative in the sense that it depends on the disease course.
Simple methods, such as the last-observation-carried-forward approach, can lead
to biased estimation. On the other hand, complex joint models require
additional assumptions on the covariate process and cannot be easily extended
to handle multiple longitudinal predictors. By incorporating sampling weights
derived from estimating the observation time process, we develop a novel
estimation procedure based on inverse-rate-weighting and kernel-smoothing for
the semiparametric proportional rate model of recurrent events. The proposed
methods do not require model specifications for the covariate processes and can
easily handle multiple time-dependent covariates. Our methods are applied to a
kidney transplant study for illustration.
