Several approaches have been proposed to model binary outcomes that arise from longitudinal studies.
Most of the approaches can be grouped into two classes : the population-averaged and subject-specific approaches.
The generalized estimating equations (GEE) method is commonly used to estimate population-averaged effects, while random-effects logistic models can be used to estimate subject-specific effects.
However, it is not clear to many epidemiologists how these two methods relate to one another or how these methods relate to more traditional stratified analysis and standard logistic models.
The authors address these issues in the context of a longitudinal smoking prevention trial, the Midwestem Prevention Project.
In particular, the authors compare results from stratified analysis, standard logistic models, conditional logistic models, the GEE models, and random-effects models by analyzing a binary outcome from two and seven repeated measurements, respectively.
In the comparison, the authors focus on the interpretation of both time-varying and time-invariant covariates under different models.
Implications of these methods for epidemiologic research are discussed.
Mots-clés Pascal : Recherche scientifique, Analyse statistique, Modèle statistique, Méthode mesure, Epidémiologie, Méthodologie, Homme, Etude longitudinale, Etude comparative, Etats Unis, Amérique du Nord, Amérique
Mots-clés Pascal anglais : Scientific research, Statistical analysis, Statistical model, Measurement method, Epidemiology, Methodology, Human, Follow up study, Comparative study, United States, North America, America
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 98-0223959
Code Inist : 002B30A01A1. Création : 11/09/1998.