![]() The Kaplan-Meier method for estimating survival functions and the Cox proportional hazards model for estimating the effects of covariates on the hazard of the occurrence of the event are commonly used statistical methods for the analysis of survival data. Others refer to such data as time-to-event data or event history data. We use the term survival data to refer to data in which the outcome variable denotes the time to the occurrence of the event of interest. Less common examples include time to shock or appropriate therapy in patients undergoing implantable cardiac defibrillator implantation or time to heart transplant in patients on a transplant waiting list. Common examples include time to death attributable to any cause, time to cause-specific death (eg, death attributable to cardiovascular causes), and time to the first of any major adverse cardiac event (MACE eg, cardiovascular death or acute myocardial infarction). Statistical software code in both R and SAS is provided.Ĭardiovascular research often focuses on outcomes that are defined as the time to the occurrence of an outcome of interest. We illustrate the application of these methods by examining cause-specific mortality in patients hospitalized with heart failure. The former family of models may be better suited for addressing etiologic questions, whereas the latter model may be better suited for estimating a patient’s clinical prognosis. 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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