Recurrent Event Data Analysis with Intermittently Observed Time-Varying Covariates

• Activity Number: 117
• Type: Topic Contributed
• Date/Time: Monday, August 10, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Shanshan Li* and Chiung-Yu Huang and Dean Follmann and Richard Krause
• Companies: Indiana University and The Johns Hopkins University and National Institute of Allergy and Infectious Diseases and NIAID/NIH
• Keywords: Estimating equations ; Kernel smoothing ; Partial likelihood ; Recurrent events ; Survival analysis

Abstract

Although recurrent event data analysis is a rapidly evolving area of research, rigorous studies on modeling and estimation of the effects of time-varying covariates on the risk of recurrent events have been lacking. Existing methods for analyzing recurrent event data usually require that the covariate processes are observed throughout the entire follow-up period. However, covariates are often observed periodically rather than continuously. We propose a novel semiparametric estimator for the regression parameters in the popular proportional rate model. The proposed estimator is based on an estimated score function where we kernel smooth the mean covariate process. We show that the proposed semiparametric estimator is asymptotically unbiased, normally distributed and derive the asymptotic variance. Simulation studies are conducted to compare the performance of the proposed estimator and the simple methods carrying forward the last covariates. The different methods are applied to an observational study designed to assess the effect of Group A streptococcus (GAS) on pharyngitis among school children in India.