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A comparison of population average and random‐effect models for the analysis of longitudinal count data with base‐line information

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  • R. Crouchley
  • R. B. Davies

Abstract

The generalized estimating equation (GEE) approach to the analysis of longitudinal data has many attractive robustness properties and can provide a ‘population average’ characterization of interest, for example, to clinicians who have to treat patients on the basis of their observed characteristics. However, these methods have limitations which restrict their usefulness in both the social and the medical sciences. This conclusion is based on the premise that the main motivations for longitudinal analysis are insight into microlevel dynamics and improved control for omitted or unmeasured variables. We claim that to address these issues a properly formulated random‐effects model is required. In addition to a theoretical assessment of some of the issues, we illustrate this by reanalysing data on polyp counts. In this example, the covariates include a base‐line outcome, and the effectiveness of the treatment seems to vary by base‐line. We compare the random‐effects approach with the GEE approach and conclude that the GEE approach is inappropriate for assessing the treatment effects for these data.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssa:v:162:y:1999:i:3:p:331-347
    DOI: 10.1111/1467-985X.00139
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    Cited by:

    1. Chan, Jennifer S.K. & Leung, Doris Y.P. & Boris Choy, S.T. & Wan, Wai Y., 2009. "Nonignorable dropout models for longitudinal binary data with random effects: An application of Monte Carlo approximation through the Gibbs output," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4530-4545, October.
    2. Emilio Augusto Coelho-Barros & Jorge Alberto Achcar & Josmar Mazucheli, 2010. "Longitudinal Poisson modeling: an application for CD4 counting in HIV-infected patients," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(5), pages 865-880.
    3. José Lombardía, María & Sperlich, Stefan, 2012. "A new class of semi-mixed effects models and its application in small area estimation," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2903-2917.
    4. Yu, Lei & Tyas, Suzanne L. & Snowdon, David A. & Kryscio, Richard J., 2009. "Effects of ignoring baseline on modeling transitions from intact cognition to dementia," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3334-3343, July.
    5. 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.
    6. Maria Jose Murcia, 2021. "Progressive and Rational CSR as Catalysts of New Product Introductions," Journal of Business Ethics, Springer, vol. 174(3), pages 613-627, December.
    7. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.
    8. Thomas R. Ten Have & Beth A. Reboussin & Michael E. Miller & Allen Kunselman, 2002. "Mixed Effects Logistic Regression Models for Multiple Longitudinal Binary Functional Limitation Responses with Informative Drop-Out and Confounding by Baseline Outcomes," Biometrics, The International Biometric Society, vol. 58(1), pages 137-144, March.

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