It is well known that the GEE methodology has issues with small sample sizes due to the asymptotic properties inherent in the covariance sandwich estimator [2, 3]. Fitzmaurice et al. noted that in small or finite sample sizes, Wald tests using the Liang-Zeger sandwich estimator tend to produce p-values that are too small [3]. The sandwich estimator of variance is biased downward; that is, it underestimates the variability of parameter estimators in small sample sizes. Much research has been performed to improve the performance of GEE under these circumstances. This is evidenced in the works of Mancl and DeRouen as well as Pan [4, 5]. Rare outcomes pose a problem as well. Even with a large sample size, a rare outcome can be viewed as a small sample problem. That is, the information concerning the event of interest is, by itself, a small sample. Adding records that do not have the outcome of interest gives no additional information to the model. If an event becomes rare enough, it becomes extremely difficult to collect enough information to construct an informative regression model. The problems GEE experiences with finite sample sizes can become exacerbated when coupled with a rare outcome.
