Statistics and Its Interface

Volume 6 (2013)

Number 1

Modeling of mean-covariance structures in generalized estimating equations with dropouts

Pages: 19 – 26

DOI: http://dx.doi.org/10.4310/SII.2013.v6.n1.a3

Authors

Kun Liu (School of Mathematics, University of Manchester, United Kingdom)

Tapio Nummi (Tampere School of Public Health, University of Tampere, Finland)

Jianxin Pan (School of Mathematics, University of Manchester, United Kingdom)

Abstract

Within the framework of joint mean-covariance models, we study the effects of dropout missing at random (MAR) on the estimation of mean and covariance structures for longitudinal data using generalized estimating equations (GEE). It is evidential that the MAR dropout has more severe influences on the estimation of variance-covariances relative to the mean estimation, as the former involves the estimation of the second moments. We propose to use the inverse probability weighted generalized estimating equation (WGEE) method to model the mean and covariance structures, simultaneously, in order to accommodate the effects of MAR dropout. The proposed WGEE approach produces unbiased estimators of parameters in both the mean and covariances for longitudinal data with MAR dropout. Simulation studies are conducted to assess the performance of the proposed approach and a real data analysis for the PANSS data is made to illustrate the effectiveness of the proposed method.

Keywords

dropout, joint mean and covariance model, longitudinal data, missing at random, weighted generalized estimating equation

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