Generalised Estimating Equations models, proposed by Liang and Zeger in 1986, are probably the simplest method for analysing data collected in groups where observations within a group may be correlated but observations in separate groups are independent. A complete description of the method is given in their two 1986 papers. The basic principle of the method is a generalisation of the fact that weighted least squares analyses give unbiased parameter estimates no matter what weights are used. Generalised linear models, such as logistic regression, have similar robustness properties, giving asymptotically correct parameter estimates even when the data are correlated. This means that it is possible to estimate regression parameters using any convenient or plausible assumptions about the true correlation between observations and get the right answer even when the assumptions are not correct. It is only necessary to use a ``model-robust'' or ``agnostic'' estimate of the standard errors. It would be unreasonable to expect this freedom of choice to be without cost and it turns out that there is a moderate gain in efficiency resulting from choosing a working correlation structure close to the true one.
Useful references include the two original papers (Zeger & Liang 1986, Liang & Zeger 1986) and two recent books: Diggle, Liang & Zeger (1993) and Fahrmeir & Tutz (1995). As far as I know the most elementary treatment anywhere in the literature is still Zeger & Liang (1986).
Section 2 gives an overview of the theory and use of Generalised Estimating Equations. Section 3 describes how to use the Lisp-Stat code, including diagnostics. Finally there is a brief discussion of missing data handling and of other software for fitting GEE models. Appendix A describes some aspects of the implementation, including the global variables (Table 3) that control many program options.