The GEE methods are fairly robust and compensate for correlation among repeated measures or clustered data. However, in rare event and finite sample size settings, the variances and covariances generated by these models are underestimated and lead to erroneous inferences. Other investigators have proposed corrections for rare events and finite sample sizes with correlated data but there is no universally agreed upon solution for dealing with these circumstances. These solutions have resulted in alternative sandwich estimators that still have performance issues.
