title: Models for longitudinal data: a generalized estimating equation approach.
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.
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland 21205.
authors.forename: S L
authors.initials: SL
authors.lastname: Zeger
authors.forename: K Y
authors.initials: KY
authors.lastname: Liang
authors.forename: P S
authors.initials: PS
authors.lastname: Albert
mesh_terms: D002648:Child; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D009035:Mothers; D012044:Regression Analysis; D012141:Respiratory Tract Infections / Q000453:epidemiology; D012907:Smoking