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

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Listed:
  • H. Zhang
  • Q. Yu
  • C. Feng
  • D. Gunzler
  • P. Wu
  • X. M. Tu

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.

Suggested Citation

  • H. Zhang & Q. Yu & C. Feng & D. Gunzler & P. Wu & X. M. Tu, 2012. "A new look at the difference between the GEE and the GLMM when modeling longitudinal count responses," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(9), pages 2067-2079, June.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:9:p:2067-2079
    DOI: 10.1080/02664763.2012.700452
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    References listed on IDEAS

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    1. D. B. Dunson, 2000. "Bayesian latent variable models for clustered mixed outcomes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 355-366.
    2. Dunson, David B., 2003. "Dynamic Latent Trait Models for Multidimensional Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 555-563, January.
    3. R. Crouchley & R. B. Davies, 1999. "A comparison of population average and random‐effect models for the analysis of longitudinal count data with base‐line information," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(3), pages 331-347.
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    Cited by:

    1. N. Lu & T. Chen & P. Wu & D. Gunzler & H. Zhang & H. He & X.M. Tu, 2014. "Functional response models for intraclass correlation coefficients," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(11), pages 2539-2556, November.
    2. Morgunov, V.I. (Моргунов, В.И.), 2016. "The Liquidity Management of the Banking Sector and the Short-Term Money Market Interest Rates [Управление Ликвидностью Банковского Сектора И Краткосрочной Процентной Ставкой Денежного Рынка]," Working Papers 21311, Russian Presidential Academy of National Economy and Public Administration.
    3. Bei Wang & Jeffrey R. Wilson, 2018. "Comparative GMM and GQL logistic regression models on hierarchical data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(3), pages 409-425, February.

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