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Biometrics. 1988 Dec;44(4):1049-60.

Models for longitudinal data: a generalized estimating equation approach.

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Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland 21205.

Erratum in

  • Biometrics 1989 Mar;45(1):347.


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.

[Indexed for MEDLINE]

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