The single covariate model was fit on a series of simulated datasets with outcomes of differing prevalence values (0.01, 0.05, 0.10, 0.30, 0.50). The simulations were run with data sizes of 500, 100, 50, 30, and 20 subjects. The various estimators’ performance was also compared when the simulated within- cluster correlation structure was either exchangeable or autoregressive, with correlation set at 0.005 or 0.05, and when observations within clusters were simulated to be independent. All simulations involve balanced designs with four observations per subject. These correlation values were selected based on the relationship between the prevalence of the outcome and the correlation among longitudinal measures. That is, the probability of the outcome restricts the range of possible correlation values [3]. Due to this relationship between the prevalence and correlation, it was not practical to simulate all combinations of prevalence values and correlations.
