American Journal of Epidemiology Vol. 147, No. 7: 694-703
Copyright © 1998 by The Johns Hopkins University School of Hygiene and Public Health
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Comparison of Population-Averaged and Subject-Specific Approaches for Analyzing Repeated Binary Outcomes
1 Department of Nutrition, Harvard School of Public Health Boston, MA
2 Epidemiology and Biostatistics Division, School of Public Health, University of Illinois at Chicago Chicago, IL
3 Prevention Research Center, University of Illinois at Chicago Chicago, IL
4 Department of Prevention Medicine, University of Southern California Los Angeles, CA
Reprint requests to Dr. Frank B. Hu, Dept. of Nutrition, Harvard School of Public Health, 655 Huntington Ave., Boston, MA 02115.
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 Midwestern 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. Am J Epidemiol 1998;147: 694–703.
generalized estimating equations; longitudinal studies; random-effects regression models; repeated measurement
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