Empirical Likelihood-Based Method Using Calibration for Longitudinal Data with Drop-Out

• Activity Number: 379
• Type: Invited
• Date/Time: Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Section on Health Policy Statistics
• Author(s): Xiao-Hua "Andrew" Zhou*+ and Baojiang Chen
• Companies: University of Washington and University of Nebraska
• Keywords: missing-data ; calibration ; empirical likelihood ; longitudinal data

Abstract

In longitdinal studies, interest often lies in estimation of the population-level relationship between the explanatory variables and dependent variables. In this talk we propose an empirical likelihood-based method to incorporate population level information in a longitudinal study with drop-out. The population-level information is incorporated via constraints on functions of the parameters, and non-random drop-out bias is corrected by using a weighted generalized estimating equations method. We provide a three-step estimation procedure that makes computation easier. Several methods that are often used in practice are compared in simulation studies, which demonstrate that our proposed method can correct the non-random drop-out bias and increase the estimation efficiency, especially for small sample size or when the missing proportion is high. Also, the proposed method is robust to misspecification of the working correlation matrix or the missing data model under the missing at random mechanism. Finally, we apply this method to an Alzheimer's disease study. This is a joint work with Dr. Baojing Chen and Gary Chan.