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Cited by 420

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Comparison of Population-Averaged and Subject-Specific Approaches for Analyzing Repeated Binary Outcomes

Hu, F. B., Goldberg, J., Hedeker, D., Flay, B. R., Pentz, M. A.·1998·American Journal of Epidemiology

Abstract

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.

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Metadata

Authors
Hu, F. B., Goldberg, J., Hedeker, D., Flay, B. R., & Pentz, M. A.
Venue
American Journal of Epidemiology
Published
1998-04-01
Volume
147
Issue
7
Pages
694-703
ISSN
  • 0002-9262 (ISSN-L)
  • 1476-6256
OpenAlex
W2104694481
Cited by
420
References
4
FWCI
7.01
Type
journal article
Language
en
Open access
closed

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.