Abstract
Recurrent event data are quite common in biomedical and epidemiological studies. A significant portion of these data also contain additional longitudinal information on surrogate markers. Previous studies have shown that popular methods using a Cox model with longitudinal outcomes as time-dependent covariates may lead to biased results, especially when longitudinal outcomes are measured with error. Hence, it is important to incorporate longitudinal information into the analysis properly. To achieve this, we model the correlation between longitudinal and recurrent event processes using latent random effect terms. We then propose a two-stage conditional estimating equation approach to model the rate function of recurrent event process conditioned on the observed longitudinal information. The performance of our proposed approach is evaluated through simulation. We also apply the approach to analyze cocaine addiction data collected by the University of Connecticut Health Center. The data include recurrent event information on cocaine relapse and longitudinal cocaine craving scores. Copyright © 2016 John Wiley & Sons, Ltd.
