![]() ![]() ![]() The latter allows one to estimate the effect of covariates on the absolute risk of the outcome over time. The former allows one to estimate the effect of the covariates on the rate of occurrence of the outcome in those subjects who are currently event free. ![]() When fitting regression models in the presence of competing risks, researchers can choose from 2 different families of models: modeling the effect of covariates on the cause-specific hazard of the outcome or modeling the effect of covariates on the cumulative incidence function. The use of the Kaplan-Meier survival function results in estimates of incidence that are biased upward, regardless of whether the competing events are independent of one another. ![]() When estimating the crude incidence of outcomes, analysts should use the cumulative incidence function, rather than the complement of the Kaplan-Meier survival function. In a study examining time to death attributable to cardiovascular causes, death attributable to noncardiovascular causes is a competing risk. A competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. Customer Service and Ordering InformationĬompeting risks occur frequently in the analysis of survival data.Stroke: Vascular and Interventional Neurology.Journal of the American Heart Association (JAHA).Circ: Cardiovascular Quality & Outcomes.Arteriosclerosis, Thrombosis, and Vascular Biology (ATVB). ![]()
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