When estimating cumulative incidence probabilities for a time to event outcome of interest, such as a major adverse cardiovascular event (MACE), competing risks, such as a non-cardiovascular death or a renal event, are often present. Multistate models and Aalen Johansen plots are useful tools for addressing competing risks in time to event analyses. However, the performance of methods to estimate confidence bands for the cumulative incidence probabilities is an open question, particularly when used with existing and modern inverse propensity score weighting methods (IPW). We test the performance of various methods over a range of event rates, samples sizes, and degrees of baseline covariate imbalance, which impact the IPW. This research is motivated by an electronic health record derived study of nearly one-hundred thousand US veterans comparing the effects of the anti-diabetic medications metformin and sulfonylureas after reaching a renal function threshold. Patients reaching the renal threshold have a high risk of experiencing MACE and non-cardiovascular death.