Linked Life Data

20515278

Source:http://linkedlifedata.com/resource/pubmed/id/20515278

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2010-6-2
pubmed:abstractText
Association and classification models differ fundamentally in objectives, measurements, and clinical context specificity. Association studies aim to identify biomarker association with disease in a study population and provide etiologic insights. Common association measurements are odds ratio, hazard ratio, and correlation coefficient. Classification studies aim to evaluate biomarker use in aiding specific clinical decisions for individual patients. Common classification measurements are sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Good association is usually a necessary, but not a sufficient, condition for good classification. Methods for developing classification models have mainly used the criteria for association models, usually minimizing total classification error without consideration of clinical application settings, and therefore are not optimal for classification purposes. We suggest that developing classification models by focusing on the region of receiver operating characteristic (ROC) curve relevant to the intended clinical application optimizes the model for the intended application setting.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
0085-591X
pubmed:author
pubmed:issnType
Print
pubmed:volume
242
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
53-8
pubmed:dateRevised
2011-11-17
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Classification versus association models: should the same methods apply?
pubmed:affiliation
Biostatistics and Biomathematics Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. zfeng@fhcrc.org
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural