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GEE for longitudinal ordinal data: Comparing R-geepack, R-multgee, R-repolr, SAS-GENMOD, SPSS-GENLIN

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  • Nooraee, Nazanin
  • Molenberghs, Geert
  • van den Heuvel, Edwin R.

Abstract

Studies in epidemiology and social sciences are often longitudinal and outcome measures are frequently obtained by questionnaires in ordinal scales. To understand the relationship between explanatory variables and outcome measures, generalized estimating equations can be applied to provide a population-averaged interpretation and address the correlation between outcome measures. It can be performed by different software packages, but a motivating example showed differences in the output. This paper investigated the performance of GEE in R (version 3.0.2), SAS (version 9.4), and SPSS (version 22.0.0) using simulated data under default settings. Multivariate logistic distributions were used in the simulation to generate correlated ordinal data. The simulation study demonstrated substantial bias in the parameter estimates and numerical issues for data sets with relative small number of subjects. The unstructured working association matrix requires larger numbers of subjects than the independence and exchangeable working association matrices to reduce the bias and diminish numerical issues. The coverage probabilities of the confidence intervals for fixed parameters were satisfactory for the independence and exchangeable working association matrix, but they were frequently liberal for the unstructured option. Based on the performance and the available options, SPSS and multgee, and repolr in R all perform quite well for relatively large sample sizes (e.g. 300 subjects), but multgee seems to do a little better than SPSS and repolr in most settings.

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  • Nooraee, Nazanin & Molenberghs, Geert & van den Heuvel, Edwin R., 2014. "GEE for longitudinal ordinal data: Comparing R-geepack, R-multgee, R-repolr, SAS-GENMOD, SPSS-GENLIN," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 70-83.
    • Handle: RePEc:eee:csdana:v:77:y:2014:i:c:p:70-83
    DOI: 10.1016/j.csda.2014.03.009
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    1. Zengri Wang, 2003. "Matching conditional and marginal shapes in binary random intercept models using a bridge distribution function," Biometrika, Biometrika Trust, vol. 90(4), pages 765-775, December.
    2. Molenberghs, Geert & Kenward, Michael G., 2010. "Semi-parametric marginal models for hierarchical data and their corresponding full models," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 585-597, February.
    3. Oster, Robert A. & Hilbe, Joseph M., 2008. "An Examination of Statistical Software Packages for Parametric and Nonparametric Data Analyses Using Exact Methods," The American Statistician, American Statistical Association, vol. 62, pages 74-84, February.
    4. Li, Yonghai & Schafer, Daniel W., 2008. "Likelihood analysis of the multivariate ordinal probit regression model for repeated ordinal responses," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3474-3492, March.
    5. Højsgaard, Søren & Halekoh, Ulrich & Yan, Jun, 2005. "The R Package geepack for Generalized Estimating Equations," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 15(i02).
    6. N. Rao Chaganty & Harry Joe, 2006. "Range of correlation matrices for dependent Bernoulli random variables," Biometrika, Biometrika Trust, vol. 93(1), pages 197-206, March.
    7. Anestis Touloumis & Alan Agresti & Maria Kateri, 2013. "GEE for Multinomial Responses Using a Local Odds Ratios Parameterization," Biometrics, The International Biometric Society, vol. 69(3), pages 633-640, September.
    8. Yan, Jun, 2007. "Enjoy the Joy of Copulas: With a Package copula," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 21(i04).
    9. Wei Pan, 2001. "Akaike's Information Criterion in Generalized Estimating Equations," Biometrics, The International Biometric Society, vol. 57(1), pages 120-125, March.
    10. Alan Agresti & Ranjini Natarajan, 2001. "Modeling Clustered Ordered Categorical Data: A Survey," International Statistical Review, International Statistical Institute, vol. 69(3), pages 345-371, December.
    11. Ivy Liu & Alan Agresti, 2005. "The analysis of ordered categorical data: An overview and a survey of recent developments," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 14(1), pages 1-73, June.
    12. N. R. Parsons & R. N. Edmondson & S. G. Gilmour, 2006. "A generalized estimating equation method for fitting autocorrelated ordinal score data with an application in horticultural research," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(4), pages 507-524, August.
    13. N. Rao Chaganty & Harry Joe, 2004. "Efficiency of generalized estimating equations for binary responses," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 851-860, November.
    14. Parsons, Nick R. & Costa, Matthew L. & Achten, Juul & Stallard, Nigel, 2009. "Repeated measures proportional odds logistic regression analysis of ordinal score data in the statistical software package R," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 632-641, January.
    15. Nores, Maria Laura & Diaz, Maria del Pilar, 2008. "Some properties of regression estimators in GEE models for clustered ordinal data," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3877-3888, March.
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