Models for longitudinal data: a generalized estimating equation approach |
| |
Authors: | S L Zeger K Y Liang P S Albert |
| |
Affiliation: | Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland 21205. |
| |
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. |
| |
Keywords: | |
|
|