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

American Journal of Epidemiology · 2003 · Vol. 157(4) · pp. 364–375
James A. Hanley

Abstract

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.

Statistical Methods and Bayesian InferenceGenetic and phenotypic traits in livestockStatistical Methods and InferenceGeeGeneralized estimating equationBinary dataBinary numberMultivariate statisticsSimple (philosophy)Orientation (vector space)Set (abstract data type)Longitudinal dataData set

MeSH terms

Body HeightChildEpidemiologic MethodsHumansLongitudinal StudiesSocioeconomic FactorsModels, Statistical

Funding

  • National Institutes of Health
  • Natural Sciences and Engineering Research Council of Canada
Citations
2,179
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19.97
field-weighted impact
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39
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100%
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Citations per year
References
Multilevel Statistical Models
Technometrics · 2006 · 4,983 citations
Statistical Aspects of the Analysis of Data From Retrospective Studies of Disease
JNCI Journal of the National Cancer Institute · 1959 · 14,848 citations
Statistical Methods in Medical Research.
Biometrics · 1972 · 4,072 citations
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