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Generalized estimating equations. Notes on the choice of the working correlation matrix.
1Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Germany. ziegler@imbs.uni-luebeck.de
Methods of Information in Medicine
|September 28, 2010
Summary
Generalized estimating equations (GEE) offer a flexible approach for correlated data. This study addresses concerns about GEE validity with dichotomous variables, proposing practical solutions for range restriction issues.
Area of Science:
- Biostatistics
- Statistical Modeling
Background:
- Generalized estimating equations (GEE) extend generalized linear models (GLM) by accounting for observation correlations.
- GEE's strength lies in not requiring correct multivariate distribution specification, only the mean structure.
Purpose of the Study:
- To address concerns regarding the validity and efficiency of GEE for dichotomous dependent variables.
- To summarize theoretical findings and provide practical solutions for GEE application in specific scenarios.
Main Methods:
- Formal introduction to GEE methodology.
- Summary of findings on selecting the working correlation matrix.
- Identification of a dilemma in optimal working correlation matrix choice for dichotomous outcomes.
Main Results:
- Presentation of biological and statistical rationale for choosing specific working correlation matrices.
- Description of three distinct methods to overcome correlation coefficient range restriction.
Conclusions:
- The discussed approaches offer a simple and practical framework for applying GEE models with dichotomous dependent variables.
- These methods effectively address range restriction issues in GEE analyses.
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