Topics on Analyzing Recurrent Event Data with Sparsely Observed Longitudinal Information

• Activity Number: 139
• Type: Contributed
• Date/Time: Monday, August 2, 2010 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Ye Shen*+ and Yongtao Guan
• Companies: Yale University and Yale University
• Keywords: Estimating Equation ; Longitudinal Data ; Recurrent Event Data ; Random Effect

Abstract

Recurrent event data are quite common in biomedical and epidemiological studies. A large portion of these data also contain sparsely observed longitudinal information on surrogate markers. It has been shown that popular methods using Cox model with longitudinal outcomes as time dependent covariates may lead to biased results. Thus, how to appropriately incorporate longitudinal information into recurrent event models becomes an important research question. Our research is motivated by a psychiatry study conducted at The Yale Stress Center. In the study, we have both longitudinal and recurrent event data. Based on a framework which models the correlation between processes using latent random effect terms, we propose an estimating equation method to model the rate function of recurrent event process conditional on sparsely observed longitudinal information.