  A SAS macro, GEECORR, has been developed for the analysis of correlated
binary data based on the Prentice (1988) estimating equations method that
extends the Liang and Zeger (1986) generalized estimating equations (GEE)
method to include additional estimating equations for the pairwise correlation
between binary variates. This extension allows for flexible modeling of both
the marginal mean and within-cluster correlation as a function of their
respective covariate risk factors. This paper provides an overview of the
extended estimating equations method, describes the features and capabilities
of the GEECORR macro, and applies the GEECORR macro to three different
datasets. In addition, this paper describes the more detailed fitting algorithm
proposed by Prentice (1988), of which a variation has been implemented in the
GEECORR macro. We provide a small simulation study to demonstrate the
efficiency of the detailed method for estimating correlation parameters.
