Linked Life Data

19367167

Source:http://linkedlifedata.com/resource/pubmed/id/19367167

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2009-6-16
pubmed:abstractText
Clinical decision-making often relies on a subject's absolute risk of a disease event of interest. However, in a frail population, competing risk events may preclude the occurrence of the event of interest. We review competing-risk regression models with a view toward predictive modeling. We show how measures of prognostic performance (such as calibration and discrimination) can be adapted to the competing-risks setting. An example of coronary heart disease (CHD) prediction in women aged 55-90 years in the Rotterdam study is used to illustrate the proposed methods, and to compare the Fine and Gray regression model to 2 alternative approaches: (1) a standard Cox survival model, which ignores the competing risk of non-CHD death, and (2) a cause-specific hazards model, which combines proportional hazards models for the event of interest and the competing event. The Fine and Gray model and the cause-specific hazards model perform similarly. However, the standard Cox model substantially overestimates 10-year risk of CHD; it classifies 18% of the individuals as high risk (>20%), compared with only 8% according to the Fine and Gray model. We conclude that competing risks have to be considered explicitly in frail populations such as the elderly.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
1531-5487
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
20
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
555-61
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Prognostic models with competing risks: methods and application to coronary risk prediction.
pubmed:affiliation
Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel, Basel, Switzerland. mwolbers@oucru.org
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't