Introduction to the Analysis of Survival Data in the Presence of Competing Risks

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 co...

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Published in	Circulation (New York, N.Y.) Vol. 133; no. 6; pp. 601 - 609
Main Authors	Austin, Peter C., Lee, Douglas S., Fine, Jason P.
Format	Journal Article
Language	English
Published	United States by the American College of Cardiology Foundation and the American Heart Association, Inc 09.02.2016 Lippincott Williams & Wilkins
Subjects	Aged Aged, 80 and over Cardiovascular Diseases - diagnosis Cardiovascular Diseases - mortality Databases, Factual - statistics & numerical data Databases, Factual - trends Female Humans Kaplan-Meier Estimate Male Models, Statistical Risk Factors Statistical Primer for Cardiovascular Research Statistics as Topic - methods Survival Analysis risk assessment cumulative incidence function models, statistical survival analysis data interpretation, statistical incidence proportional hazards models
Online Access	Get full text
ISSN	0009-7322 1524-4539 1524-4539
DOI	10.1161/CIRCULATIONAHA.115.017719

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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 modelsmodeling 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.
AbstractList	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. Supplemental Digital Content is available in the text. 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. 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 modelsmodeling 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. 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.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.
Author	Austin, Peter C. Fine, Jason P. Lee, Douglas S.
AuthorAffiliation	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.)
AuthorAffiliation_xml	– name: 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.)
Author_xml	– sequence: 1 givenname: Peter surname: Austin middlename: C. fullname: Austin, Peter C. organization: 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.) – sequence: 2 givenname: Douglas surname: Lee middlename: S. fullname: Lee, Douglas S. – sequence: 3 givenname: Jason surname: Fine middlename: P. fullname: Fine, Jason P.
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/26858290$$D View this record in MEDLINE/PubMed
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Snippet	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... Supplemental Digital Content is available in the text. Competing risks occur frequently in the analysis of survival data. A competing risk is an event whose...
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SubjectTerms	Aged Aged, 80 and over Cardiovascular Diseases - diagnosis Cardiovascular Diseases - mortality Databases, Factual - statistics & numerical data Databases, Factual - trends Female Humans Kaplan-Meier Estimate Male Models, Statistical Risk Factors Statistical Primer for Cardiovascular Research Statistics as Topic - methods Survival Analysis
Title	Introduction to the Analysis of Survival Data in the Presence of Competing Risks
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