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Working correlation structure selection in GEE analysis

Author

Listed:
  • María Carmen Pardo

    (Complutense University of Madrid)

  • Rosa Alonso

    (Complutense University of Madrid)

Abstract

The method of generalized estimating equations models the association between the repeated observations on a subject. It is important to select an appropriate working correlation structure for the repeated measures per subject in order to enhance efficiency of estimation of the regression parameter. Some existing selection criteria choose the structure for which the covariance matrix estimator and the specified working covariance matrix are closest. So, we define a new criterion based on this idea for selecting a working correlation structure. Also, we compare our criterion with some existing criteria to identify the true correlation structure via simulations for Poisson, binomial and normal responses, and exchangeable or AR(1) intracluster correlation structure. We assume that for each subject, the number of observations remains the same. Furthermore, we also illustrate the performance of our criterion using two data sets. Finally, we conclude that our approach is a good selection criterion in most of the different considered settings.

Suggested Citation

  • María Carmen Pardo & Rosa Alonso, 2019. "Working correlation structure selection in GEE analysis," Statistical Papers, Springer, vol. 60(5), pages 1447-1467, October.
    • Handle: RePEc:spr:stpapr:v:60:y:2019:i:5:d:10.1007_s00362-017-0881-0
    DOI: 10.1007/s00362-017-0881-0
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    References listed on IDEAS

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    1. Brajendra C. Sutradhar & Kalyan Das, 2000. "On the Accuracy of Efficiency of Estimating Equation Approach," Biometrics, The International Biometric Society, vol. 56(2), pages 622-625, June.
    2. Wei Pan, 2001. "Akaike's Information Criterion in Generalized Estimating Equations," Biometrics, The International Biometric Society, vol. 57(1), pages 120-125, March.
    3. You-Gan Wang, 2003. "Working correlation structure misspecification, estimation and covariate design: Implications for generalised estimating equations performance," Biometrika, Biometrika Trust, vol. 90(1), pages 29-41, March.
    4. Anthony Y. C. Kuk, 2003. "A generalized estimating equation approach to modelling foetal response in developmental toxicity studies when the number of implants is dose dependent," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(1), pages 51-61, January.
    5. James Cui, 2007. "QIC program and model selection in GEE analyses," Stata Journal, StataCorp LP, vol. 7(2), pages 209-220, June.
    6. Hu Yang & Chaohui Guo & Jing Lv, 2016. "Variable selection for generalized varying coefficient models with longitudinal data," Statistical Papers, Springer, vol. 57(1), pages 115-132, March.
    7. Park, Taesung & Davis, Charles S. & Li, Ning, 1998. "Alternative Gee estimation procedures for discrete longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 28(3), pages 243-256, September.
    8. Jokinen, Jukka, 2006. "Fast estimation algorithm for likelihood-based analysis of repeated categorical responses," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1509-1522, December.
    9. Hin, Lin-Yee & Carey, Vincent J. & Wang, You-Gan, 2007. "Criteria for WorkingCorrelationStructure Selection in GEE: Assessment via Simulation," The American Statistician, American Statistical Association, vol. 61, pages 360-364, November.
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