Chapter FIFTEEN. Modeling Correlated Outcomes with Generalized Estimating Equations
Published Online: 11 AUG 2003
DOI: 10.1002/0471467979.ch15
Copyright © 2003 John Wiley & Sons, Inc.
Book Title
Quantitative Methods in Population Health: Extensions of Ordinary Regression
Additional Information
How to Cite
Palta, M. (2003) Modeling Correlated Outcomes with Generalized Estimating Equations, in Quantitative Methods in Population Health: Extensions of Ordinary Regression, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/0471467979.ch15
Publication History
- Published Online: 11 AUG 2003
- Published Print: 15 AUG 2003
Book Series:
Book Series Editors:
- Walter A. Shewhart,
- Samuel S. Wilks
ISBN Information
Print ISBN: 9780471455059
Online ISBN: 9780471467977
- Summary
- Chapter
Keywords:
- Poisson;
- binary;
- GEE;
- empirical variance;
- conditional logistic regression;
- correlation;
- longitudinal;
- drop-out;
- pattern mixture;
- PROC GENMOD
Summary
We consider correlated or longitudinal binomial and Poisson outcomes. Motivation for generalized estimating equations (GEE) based on score equations in previous chapters. Empirical variance formula with GEE. Comparison of PROC MIXED and PROC GENMOD for normally distributed data. Application of PROC GENMOD with different correlation structures. Analysis of longitudinal hypertension data as example of correlated binary outcome. Analysis of longitudinal hospitalization data as example of Poisson outcome. Hypertension data analyses include example of conditional logistic regression as alternative to GEE. Consideration of how to adjust for drop-out via pattern mixture model.