The GEE Procedure

References

  • Carey, V., Zeger, S. L., and Diggle, P. J. (1993). “Modelling Multivariate Binary Data with Alternating Logistic Regressions.” Biometrika 80:517–526.

  • Diggle, P. J., Heagerty, P., Liang, K.-Y., and Zeger, S. L. (2002). Analysis of Longitudinal Data. 2nd ed. New York: Oxford University Press.

  • Diggle, P. J., Liang, K.-Y., and Zeger, S. L. (1994). Analysis of Longitudinal Data. Oxford: Clarendon Press.

  • Fitzmaurice, G. M., Laird, N. M., and Ware, J. H. (2011). Applied Longitudinal Analysis. Hoboken, NJ: John Wiley & Sons.

  • Fitzmaurice, G. M., Molenberghs, G., and Lipsitz, S. R. (1995). “Regression Models for Longitudinal Binary Responses with Informative Drop-Outs.” Journal of the Royal Statistical Society, Series B 57:691–704.

  • Hardin, J. W., and Hilbe, J. M. (2003). Generalized Estimating Equations. Boca Raton, FL: Chapman & Hall/CRC.

  • Heagerty, P., and Zeger, S. L. (1996). “Marginal Regression Models for Clustered Ordinal Measurements.” Journal of the American Statistical Association 91:1024–1036.

  • Hurlbut, M. S., Wood, P. A., and Hough, R. L. (1996). “Providing Independent Housing for the Homeless Mentally Ill: A Novel Approach to Evaluating Long-Term Longitudinal Housing Patterns.” Journal of Community Psychology 24:291–310.

  • Liang, K.-Y., and Zeger, S. L. (1986). “Longitudinal Data Analysis Using Generalized Linear Models.” Biometrika 73:13–22.

  • Lipsitz, S. R., Fitzmaurice, G. M., Orav, E. J., and Laird, N. M. (1994). “Performance of Generalized Estimating Equations in Practical Situations.” Biometrics 50:270–278.

  • Lipsitz, S. R., Kim, K., and Zhao, L. (1994). “Analysis of Repeated Categorical Data Using Generalized Estimating Equations.” Statistics in Medicine 13:1149–1163.

  • Mallinckrodt, C. (2013). Preventing and Treating Missing Data in Longitudinal Clinical Trials: A Practical Guide. Cambridge: Cambridge University Press.

  • McCullagh, P., and Nelder, J. A. (1989). Generalized Linear Models. 2nd ed. London: Chapman & Hall.

  • Molenberghs, G., and Kenward, M. G. (2007). Missing Data in Clinical Studies. New York: John Wiley & Sons.

  • O’Kelly, M., and Ratitch, B. (2014). Clinical Trials with Missing Data: A Guide for Practitioners. Chichester, UK: John Wiley & Sons.

  • Pan, W. (2001). “Akaike’s Information Criterion in Generalized Estimating Equations.” Biometrics 57:120–125.

  • Preisser, J. S., Lohman, K. K., and Rathouz, P. J. (2002). “Performance of Weighted Estimating Equations for Longitudinal Binary Data with Drop-Outs Missing at Random.” Statistics in Medicine 21:3035–3054.

  • Robins, J. M., and Rotnitzky, A. (1995). “Semiparametric Efficiency in Multivariate Regression Models with Missing Data.” Journal of the American Statistical Association 90:122–129.

  • Rubin, D. B. (1976). “Inference and Missing Data.” Biometrika 63:581–592.

  • Stokes, M. E., Davis, C. S., and Koch, G. G. (2012). Categorical Data Analysis Using SAS. 3rd ed. Cary, NC: SAS Institute Inc.