Fixed effects, random effects and GEE: What are the differences?

  title={Fixed effects, random effects and GEE: What are the differences?},
  author={Joseph C. Gardiner and Zhehui Luo and Lee Anne Roman},
  journal={Statistics in Medicine},
For analyses of longitudinal repeated‐measures data, statistical methods include the random effects model, fixed effects model and the method of generalized estimating equations. We examine the assumptions that underlie these approaches to assessing covariate effects on the mean of a continuous, dichotomous or count outcome. Access to statistical software to implement these models has led to widespread application in numerous disciplines. However, careful consideration should be paid to their… 

Random-Effects, Fixed-Effects and the within-between Specification for Clustered Data in Observational Health Studies: A Simulation Study

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No quick fix: understanding the difference between fixed and random effect models

  • A. Leyland
  • Economics
    Journal of Epidemiology & Community Health
  • 2010
This commentary considers the major assumption underlying the chosen fixed effects model, details how the alternative random effects approach would allow testing of this assumption and reflects on the need for a widespread understanding of the importance of the choice of model.

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Methods that simultaneously model the data and the drop-out process within a unified model-based framework are discussed, and possible extensions outlined.

Multilevel and Longitudinal Modeling Using Stata

Preface Linear Variance-Components Models Introduction How reliable are expiratory flow measurements? The variance-components model Modeling the Mini Wright measurements Estimation methods Assigning

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