Generalized estimating equations. Notes on the choice of the working correlation matrix.
BACKGROUND
Generalized estimating equations (GEE) are an extension of generalized linear models (GLM) in that they allow adjusting for correlations between observations. A major strength of GEE is that they do not require the correct specification of the multivariate distribution but only of the mean structure.


OBJECTIVE
Several concerns have been raised about the validity of GEE when applied to dichotomous dependent variables. In this contribution, we summarize the theoretical findings concerning efficiency and validity of GEE.


METHODS
We introduce the GEE in a formal way, summarize general findings on the choice of the working correlation matrix, and show the existence of a dilemma for the optimal choice of the working correlation matrix for dichotomous dependent variables.


RESULTS
Biological and statistical arguments for choosing a specific working correlation matrix are given. Three approaches are described for overcoming the range restriction of the correlation coefficient.


CONCLUSIONS
The three approaches described in this article for overcoming the range restrictions for dichotomous dependent variables in GEE models provide a simple and practical way for use in applications.
Ziegler
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Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Germany. ziegler@imbs.uni-luebeck.de
Vens
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D000465:Algorithms; D016000:Cluster Analysis; D016014:Linear Models; D015203:Reproducibility of Results; D013223:Statistics as Topic / Q000379:methods*