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Abstract Details

Activity Number: 168
Type: Topic Contributed
Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #304776
Title: Longitudinal Data Analysis Using Conditional Empirical Likelihood
Author(s): Peisong Han*+ and Peter Song and Lu Wang
Companies: University of Michigan and University of Michigan and University of Michigan
Address: Department of Biostatistics, Ann Arbor, MI, 48109, United States
Keywords: Estimation efficiency ; Estimating equations ; Marginal model ; Model misspecification ; Variance-covariance matrix

We propose a conditional empirical likelihood (CEL) method for longitudinal data analysis within the framework of marginal models. The possible unbalanced follow-up visits are dealt with via stratification according to distinct follow-up patterns. The key innovation of our method is that it does not require any explicit modeling of the covariance of repeated measurements, but only requires a marginal mean regression model. Thus, our method is robust against misspecification of second moment structures. The proposed estimator is connected to generalized estimating equations (GEE) estimator, and achieves the same efficiency as that of GEE estimator employing true covariance matrix. Large sample properties of the proposed CEL estimator are studied. Numerical implementation and related issues are discussed. Simulation studies are conducted to assess the finite sample performance, with comparison to that of GEE estimator and generalized empirical likelihood estimator. Data collected from a longitudinal nutrition study are analyzed as application.

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