title: Introduction to the Analysis of Survival Data in the Presence of Competing Risks.
abstract: Competing risks occur frequently in the analysis of survival data. A competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. In a study examining time to death attributable to cardiovascular causes, death attributable to noncardiovascular causes is a competing risk. When estimating the crude incidence of outcomes, analysts should use the cumulative incidence function, rather than the complement of the Kaplan-Meier survival 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 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 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. The latter allows one to estimate the effect of covariates on the absolute risk of the outcome over time. 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. We illustrate the application of these methods by examining cause-specific mortality in patients hospitalized with heart failure. Statistical software code in both R and SAS is provided.
authors: From Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada (P.C.A., D.S.L.); Institute of Health Management, Policy and Evaluation, University of Toronto, Ontario, Canada (P.C.A., D.S.L.); Schulich Heart Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada (P.C.A.); Department of Medicine, University of Toronto, Ontario, Canada (D.S.L.); Peter Munk Cardiac Center, Department of Medicine, and the Joint Department of Medical Imaging, University Health Network, Toronto, Ontario, Canada (D.S.L.); Department of Biostatistics, University of North Carolina, Chapel Hill, NC (J.P.F.); and Department of Statistics & Operations Research, University of North Carolina, Chapel Hill (J.P.F.). peter.austin@ices.on.ca.
authors: Peter C
authors: PC
authors: Austin
authors: From Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada (P.C.A., D.S.L.); Institute of Health Management, Policy and Evaluation, University of Toronto, Ontario, Canada (P.C.A., D.S.L.); Schulich Heart Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada (P.C.A.); Department of Medicine, University of Toronto, Ontario, Canada (D.S.L.); Peter Munk Cardiac Center, Department of Medicine, and the Joint Department of Medical Imaging, University Health Network, Toronto, Ontario, Canada (D.S.L.); Department of Biostatistics, University of North Carolina, Chapel Hill, NC (J.P.F.); and Department of Statistics & Operations Research, University of North Carolina, Chapel Hill (J.P.F.).
authors: Douglas S
authors: DS
authors: Lee
authors: From Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada (P.C.A., D.S.L.); Institute of Health Management, Policy and Evaluation, University of Toronto, Ontario, Canada (P.C.A., D.S.L.); Schulich Heart Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada (P.C.A.); Department of Medicine, University of Toronto, Ontario, Canada (D.S.L.); Peter Munk Cardiac Center, Department of Medicine, and the Joint Department of Medical Imaging, University Health Network, Toronto, Ontario, Canada (D.S.L.); Department of Biostatistics, University of North Carolina, Chapel Hill, NC (J.P.F.); and Department of Statistics & Operations Research, University of North Carolina, Chapel Hill (J.P.F.).
authors: Jason P
authors: JP
authors: Fine
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D002318:Cardiovascular Diseases / Q000175:diagnosis / Q000401:mortality*; D016208:Databases, Factual / Q000706:statistics & numerical data* / Q000639:trends; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D008297:Male; D015233:Models, Statistical; D012307:Risk Factors; D013223:Statistics as Topic / Q000379:methods; D016019:Survival Analysis
keywords: cumulative incidence function; data interpretation, statistical; incidence; models, statistical; proportional hazards models; risk assessment; survival analysis