Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Note on Working Correlation in the GEE of Longitudinal Counts Data

  • Received : 20111000
  • Accepted : 20111000
  • Published : 2011.11.30
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Abstract

The method of generalized estimating equations(GEE) is widely used in the analysis of a correlated dataset that consists of repeatedly observed responses within subjects. The GEE uses a quasi-likelihood equations to find the parameter estimates without assuming a specific distribution for the correlated responses. In this paper we study the importance of specifying the working correlation structure appropriately in fitting GEE for correlated counts data. We investigate the empirical coverages of confidence intervals for the regression coefficients according to four kinds of working correlations where one structure should be specified by the users. The confidence intervals are computed based on the asymptotic normality and the sandwich variance estimator.

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References

  1. Agresti, A. (2002). Categorical Data Analysis, Second Edition, Wiley, New York.
  2. Firth, D. (1993). Recent development in quasi-likelihood methods, Proceedings of ISI 49th Session, 341-358.
  3. Halekoh, U., Hojsgaard, S. and Yan, J. (2006). The R package geepack for generalized estimating equations, Journal of Statistical Software, 15, 1-11.
  4. Jeong, K. M. (2005). Generalized linear mixed models for ordinal response in clustered data, Journal of the Korean Data Analysis Society, 7, 817-828.
  5. Kauermann, G. and Caroll, R. J. (2001). A note on the efficiency of sandwich covariance matrix estimation, Journal of the American Statistical Association, 96, 1387-1397. https://doi.org/10.1198/016214501753382309
  6. Liang, K. Y. and Zeger, S. L. (1986). Longitudinal data using generalized linear models, Biometrika, 73, 13-22. https://doi.org/10.1093/biomet/73.1.13
  7. Nores, M. L. and Diez, M. P. (2008). Some properties of regression estimates in GEE models for clustered ordinal data, Computational Statistics & Data Analysis, 52, 3877-3888. https://doi.org/10.1016/j.csda.2007.12.009
  8. R Development Core Team (2006). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-900051-07-0, URL: http://www.r-project.org.
  9. Thall, P. F. and Vail, S. C. (1990). Some covariance models for longitudinal count data with overdispersion, Biometrics, 46, 657-671. https://doi.org/10.2307/2532086
  10. Yan, J. (2002). Geepack: Yet another package for generalized estimating equations, R News, 2, 12-14.