Estimators within the Cox family are often used to estimate models for repeated events. Yet, there is much we still do not know about the performance of these estimators. In particular, we do not know how they perform given time dependence, different censoring rates, and a varying number of events and sample sizes. We use Monte Carlo simulations to demonstrate the performance of a variety of popular semi-parametric estimators as these data aspects change and under conditions of event dependence and heterogeneity, both, or neither. We conclude that the conditional frailty model outperforms other standard estimators under a wide array of data-generating processes, and data limitations rarely alter its performance.