Models for Longitudinal Data: A Generalized Estimating Equation Approach

1988/12/01 by Scott L. Zeger, Kung‐Yee Liang, Paul S. Albert · 9 citations
Mathematics · #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistical Distribution Estimation and Applications

paper · doi:10.2307/2531734

openalex publication_date 1988/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

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.

Citations

• Random-Effects Models for Longitudinal Data
• Longitudinal data analysis using generalized linear models

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