Generalized Estimating Equations

Methods Inf Med 2010; 49(05): 419-420
DOI: 10.1055/s-0038-1625134

Editorial

Schattauer GmbH

Notes on the Choice of the Working Correlation Matrix

G. Molenberghs

¹Universiteit Hasselt, Diepenbeek, Belgium and Katholieke Universiteit Leuven, Leuven, Belgium

References
1 Ziegler A, Vens M. Generalized Estimating Equations: Notes on the Choice of the Working Correlation Matrix. Methods Inf Med 2010; 49 (05) 421-425.
2 Breitung J, Chaganty NR, Daniel RM, Kenward MG, Lechner M, Martus P, Sabo RT, Wang Y-G, Zorn C. Discussion of “Generalized Estimating Equations: Notes on the Choice of the Working Correlation Matrix”. Methods Inf Med 2010; 49 (05) 426-432.
3 Zeger SL, Liang K-Y, Albert PS. Models for longitudinal data: a generalized estimating equation approach. Biometrics 1988; 44: 1049-1060.
4 Molenberghs G, Kenward MG. Semi-parametric marginal models for hierarchical data and their corresponding full models. Computational Statistics and Data Analysis 2009; 54: 585-597.
5 Molenberghs G, Verbeke G. Models for Discrete Longitudinal Data. New York: Springer; 2005
6 Molenberghs G, Kenward MG, Verbeke G, Teshome Ayele B. Pseudo-likelihood estimation for incomplete data. Statistica Sinica. (in press).
7 Beunckens C, Sotto C, Molenberghs G. A simulation study comparing weighted estimating equations with multiple imputation based estimating equations for longitudinal binary data. Computational Statistics and Data Analysis 2008; 52: 1533-1548.
8 Molenberghs G, Kenward MG. Missing Data in Clinical Studies. Chichester: John Wiley & Sons; 2007
9 Fitzmaurice GM, Molenberghs G, Lipsitz SR. Regression models for longitudinal binary responses with informative dropouts. Journal of the Royal Statistical Society, Series B 1995; 57: 691-704.