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Cited by 4,505

The DOI matches its Crossref record.

Models for Longitudinal Data: A Generalized Estimating Equation Approach

Zeger, Scott L., Liang, Kung-Yee, Albert, Paul S.·1988·Biometrics
DOI 10.2307/2531734
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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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Metadata

Authors
Zeger, S. L., Liang, K.-Y., & Albert, P. S.
Venue
Biometrics
Publisher
JSTOR
Published
1988-12
Volume
44
Issue
4
Pages
1049
ISSN
0006-341X (ISSN-L)
DOI
10.2307/2531734
OpenAlex
W1976566530
Cited by
4,505
References
0
FWCI
13.59
Type
journal article
Language
en
Open access
closed

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.