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Models for Longitudinal Data: A Generalized Estimating Equation Approach

Scott L. Zeger, Kung-Yee Liang and Paul S. Albert
Biometrics
Vol. 44, No. 4 (Dec., 1988), pp. 1049-1060
DOI: 10.2307/2531734
Stable URL: http://www.jstor.org/stable/2531734
Page Count: 12
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Models for Longitudinal Data: A Generalized Estimating Equation Approach
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Abstract

This article discusses extensions of generalized linear models for the analysis of longitudinal data. Two approaches are considered: subject-specific (SS) models in which heterogeneity in regression parameters is explicitly modelled; and population-averaged (PA) models in which the aggregate response for the population is the focus. We use a generalized estimating equation approach to fit both classes of models for discrete and continuous outcomes. When the subject-specific parameters are assumed to follow a Gaussian distribution, simple relationships between the PA and SS parameters are available. The methods are illustrated with an analysis of data on mother's smoking and children's respiratory disease.

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