This research explored a novel way of building a hybrid sandwich estimator that would achieve superior performance over that of the standard Liang-Zeger sandwich estimator in settings with low outcome prevalence and reduced sample sizes. The performance of this estimator was also compared with other sandwich estimators adjusted for improved performance in small sample sizes. As the outcome prevalence dropped below 30% and the sample size below that of 50 subjects, the choice of estimators matters, and one should consider using an alternative to the Liang-Zeger estimator. In our limited simulation settings, the Rogers sandwich estimator outperformed the Liang-Zeger and typically outperformed all other estimators as the prevalence and sample size both dropped. The Rogers estimator is an extension of the Pan estimator, which also performed very well in these simulations. The performance of the Rogers estimator is dependent on the determinant calculated in the inflation factor. It is possible that the performance of the Rogers estimator may be inferior in comparison to the Pan estimator under different correlation settings. The performance of the Mancl and DeRouen sandwich estimator deteriorated
