ABSTRACT Inverse probability of treatment weighting (IPTW) using the propensity score allows estimation of the effect of treatment in observational studies. We had three objectives: first, to describe methods for using IPTW to estimate the effects of treatments in settings with competing risks; second, to illustrate the application of these methods using empirical analyses; and third, to conduct Monte Carlo simulations to evaluate the relative performance of three methods for estimating time‐specific risk differences and time‐specific relative risks in settings with competing risks. In doing so, we provide guidance to applied biostatisticians and clinical investigators on the use of IPTW in settings with competing risks. We examined three estimators of time‐specific risk differences and relative risks: the weighted Aalen–Johansen estimator, an estimator that combines IPTW with inverse probability of censoring weights (IPTW‐IPCWs), and a double‐robust augmented IPTW estimator combined with IPCW (AIPTW‐IPCW). The design of our simulations reflected clinically realistic scenarios. Our simulations found that all three estimators tended to result in unbiased estimations of time‐specific risk differences and time‐specific relative risks. However, the weighted Aalen–Johansen estimator and the AIPTW‐IPCW estimator tended to result in estimates with greater precision compared to the IPTW‐IPCW estimator. In our empirical analyses, we illustrated the application of these methods by estimating the effect of statin prescribing on the risk of subsequent cardiovascular death in patients discharged from the hospital with a diagnosis of acute myocardial infarction.