The GEE Procedure (Experimental)

Overview: GEE Procedure

The GEE procedure implements the generalized estimating equations (GEE) approach (Liang and Zeger, 1986), which extends the generalized linear model to handle longitudinal data (Stokes, Davis, and Koch, 2012; Fitzmaurice, Laird, and Ware, 2011; Diggle et al., 2002). For longitudinal studies, missing data are common, and they can be caused by dropouts or skipped visits. If missing responses depend on previous responses, the usual GEE approach can lead to biased estimates. So the GEE procedure also implements the weighted GEE method to handle missing responses that are caused by dropouts in longitudinal studies (Robins and Rotnitzky, 1995; Preisser, Lohman, and Rathouz, 2002).

The GEE method fits a marginal model to longitudinal data. The regression parameters in the marginal model are interpreted as population-averaged. For more information about the GEE method, see Fitzmaurice, Laird, and Ware (2011); Hardin and Hilbe (2003); Diggle et al. (2002); Lipsitz et al. (1994).

The GEE procedure compares most closely to the GENMOD procedure in SAS/STAT software. Both procedures implement the standard generalized estimating equation approach for longitudinal data; this approach is appropriate for complete data or when data are missing completely at random (MCAR). When the data are missing at random (MAR), the weighted GEE method produces valid inference. Molenberghs and Kenward (2007); Fitzmaurice, Laird, and Ware (2011); Mallinckrodt (2013); O’Kelly and Ratitch (2014) describe the weighted GEE method.

This version of the GEE procedure does not provide the multinomial distribution for polytomous responses, the CLOGIT or GLOGIT link functions, diagnostics, or the alternating logistic regressions (ALR) analysis. Future releases will contain additional functionality.