A new look at the difference between the GEE and the GLMM when modeling longitudinal count responses

Abstract

Poisson log-linear regression is a popular model for count responses. We examine two popular extensions of this model – the generalized estimating equations (GEE) and the generalized linear mixed-effects model (GLMM) – to longitudinal data analysis and complement the existing literature on characterizing the relationship between the two dueling paradigms in this setting. Unlike linear regression, the GEE and the GLMM carry significant conceptual and practical implications when applied to modeling count data. Our findings shed additional light on the differences between the two classes of models when used for count data. Our considerations are demonstrated by both real study and simulated data.

Keywords:

• generalized linear mixed-effect model
• generalized estimating equations
• hotelling's T ² statistic
• likelihood ratio test
• score test

Acknowledgements

This research is supported in part by NIH grants U54 RR023480, UL1-RR024160, R21 DA027521 and R33 DA027521. We sincerely thank Ms Cheryl Bliss-Clark at the University of Rochester for her help with improving the presentation of the manuscript.