Statistical Analysis of Correlated Data Using Generalized Estimating Equations: An Orientation
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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.
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The record
- Venue
- American Journal of Epidemiology
- Topic
- Statistical Methods and Bayesian Inference
- Field
- Mathematics
- Canadian institutions
- McGill University
- Funders
- National Institute of Diabetes and Digestive and Kidney DiseasesNational Cancer InstituteNational Institute on Drug AbuseNatural Sciences and Engineering Research Council of CanadaNational Institutes of Health
- Keywords
- GeeGeneralized estimating equationBinary dataBinary numberMultivariate statisticsSimple (philosophy)Orientation (vector space)Set (abstract data type)Longitudinal dataData setStatisticsEstimating equationsComputer scienceMathematicsMultivariate analysisApplied mathematicsAlgorithmData miningMaximum likelihoodArithmetic
- Has abstract in OpenAlex
- yes