Journal Article

Statistical Analysis of Correlated Data Using Generalized Estimating Equations: An Orientation

James A. Hanley, Abdissa Negassa, Michael D. deB. Edwardes and Janet E. Forrester

in American Journal of Epidemiology

Published on behalf of Johns Hopkins Bloomberg School of Public Health

Volume 157, issue 4, pages 364-375
Published in print February 2003 | ISSN: 0002-9262
Published online February 2003 | e-ISSN: 1476-6256 | DOI: http://dx.doi.org/10.1093/aje/kwf215
Statistical Analysis of Correlated Data Using Generalized Estimating Equations: An Orientation

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The method of generalized estimating equations (GEE) is often used to analyze longitudinal and other correlated response data, particularly if responses are binary. However, few descriptions of the method are accessible to epidemiologists. In this paper, the authors use small worked examples and one real data set, involving both binary and quantitative response data, to help end-users appreciate the essence of the method. The examples are simple enough to see the behind-the-scenes calculations and the essential role of weighted observations, and they allow nonstatisticians to imagine the calculations involved when the GEE method is applied to more complex multivariate data.

Keywords: correlation; epidemiologic methods; generalized estimating equation; longitudinal studies; odds ratio; statistics; Abbreviation: GEE, generalized estimating equations.

Journal Article.  5392 words.  Illustrated.

Subjects: Public Health and Epidemiology

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