|Description||Hardin and Hilbe (2003) have written a very detailed book on the statistical methodology of generalized estimating equations (GEE). This work is very much a continuation of their previous book (Hardin and Hilbe 2001), which focused on generalized linear models (GLM): for a review in this journal, see Newson (2001). GEE is an extension to GLM that does not require independent observations and thus can be used to analyze clustered and longitudinal data. At the simplest level, a variance–covariance matrix, which describes the correlation between observations, is specified, and multivariate weighted least squares is used to estimate a GEE model. The book primarily focuses on explaining this process in detail. It assumes that the reader has a fundamental understanding of GLM, maximum likelihood estimation, and distributional statistics. As Hardin and Hilbe are the original authors of the glm and xtgee commands in Stata, which are used to estimate GLM and GEE models, respectively (Hilbe 1993), Stata is often used in the book to present various illustrative examples.
The authors also provide detailed information on estimating these models in SAS, S-Plus, and SUDAAN.||en_US