Regression models with binary outcome variables are prevalent in all research disciplines. If the data are independent, then the covariance between two measured values, which is a measure of linear dependence, is zero. If the data are dependent, Generalized Estimating Equations (GEE) can be used to account for the correlation, which is a function of the covariance among repeated or clustered measurements [1]. The GEE framework contains options for the working covariance structure based upon the assumed pattern of correlation within the data. One of the strengths of using GEE is that the sandwich or robust variance estimator produces unbiased standard errors in large sample sizes for the regression coefficients even when the covariance structure is misspecified. This is a tremendous advantage, but the sandwich estimator of variance is not without drawbacks.
