Analysis of Multiple Type Recurrent Event Data in the Presence of Terminal Events and Missing Covariate Information

• Activity Number: 596
• Type: Contributed
• Date/Time: Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Shankar Viswanathan*+ and Jianwen Cai
• Companies: Albert Einstein College of Medicine and The University of North Carolina at Chapel Hill
• Keywords: recurrent events ; missing data ; multiple-type events ; terminal events ; weighted estimating equation ; marginal rate model

Abstract

In many clinical and epidemiological studies, more than one type of recurrent events such as multiple types of infections (bacterial, viral, fungal) in immunocompromised patients occurs. Often in such studies, the interest is to examine the relationship between covariates and recurrent events. However, in many studies, some of the covariates involve missing information due to various reasons. Under such missingness, commonly practiced method is complete case analysis in which the estimated parameters may be biased or inefficient. We present a method for estimating the parameters in the marginal rate model for analyzing multiple-type recurrent events in the presence of a terminal event and missing covariate information. We adopt a weighted estimating equation approach with missing data assumed to be MAR for estimating the parameters. The parameters are estimated via weighted expectation-maximization (EM) algorithm. Simulation studies showed that our proposed estimators for the regression parameters are in general approximately unbiased and the variance estimates perform well. We applied the proposed method to the India renal transplant cohort data for illustration.