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Methods Inf Med 2010; 49(05): 426-432
DOI: 10.1055/s-0038-1625133
Original Articles
Schattauer GmbH

Discussion of “Generalized Estimating Equations: Notes on the Choice of the Working Correlation Matrix”

J. Breitung
1   University of Bonn, Bonn, Germany
,
N. R. Chaganty
2   Old Dominion University, Norfolk, VA, USA
,
R. M. Daniel
3   London School of Hygiene and Tropical Medicine, London, UK
,
M. G. Kenward
3   London School of Hygiene and Tropical Medicine, London, UK
,
M. Lechner
4   University of St. Gallen, St. Gallen, Switzerland
,
P. Martus
5   Charité – Universitätsmedizin Berlin, Berlin, Germany
,
R. T. Sabo
6   Virginia Commonwealth University, Richmond, VA, USA
,
Y.-G. Wang
7   The University of Queensland, St. Lucia, Queensland, Australia
,
C. Zorn
8   Pennsylvania State University, University Park, PA, USA
› Author Affiliations
Further Information

Publication History





Publication Date:
20 January 2018 (online)

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Summary

Objective: To discuss generalized estimating equations as an extension of generalized linear models by commenting on the paper of Ziegler and Vens “Generalized Estimating Equations: Notes on the Choice of the Working Correlation Matrix”.

Methods: Inviting an international group of experts to comment on this paper.

Results: Several perspectives have been taken by the discussants. Econometricians have established parallels to the generalized method of moments (GMM). Statisticians discussed model assumptions and the aspect of missing data. Applied statisticians commented on practical aspects in data analysis.

Conclusions: In general, careful modeling correlation is encouraged when considering estimation efficiency and other implications, and a comparison of choosing instruments in GMM and generalized estimating equations (GEE) would be worthwhile. Some theoretical drawbacks of GEE need to be further addressed and require careful analysis of data. This particularly applies to the situation when data are missing at random.

Keywords

Correlation matrix - generalized estimating equations - independence estimating equations - restriction of parameter space
 

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