When the simulated correlation is .05, a similar trend can be seen for the regression coefficient (Figure 3). As the sample size and prevalence decrease, the estimators begin to diverge from one another in their performance. Typically, the Liang-Zeger estimator lags behind the others as it underestimates the variance, which decreases its coverage probability. At an outcome prevalence of 10%, our proposed estimator performs better than the other estimators. It is interesting to note that when a simulated outcome prevalence is as low as 10% is coupled with a sample size of 100, all the sandwich estimators underestimate the true variance. As the outcome prevalence increases to 50%, our estimator slightly overestimates the variance at sample sizes of 50 subjects or fewer.
