Integrating the predictiveness of a marker with its performance as a classifier

There are two popular statistical approaches to biomarker evaluation. One models the risk of disease (or disease outcome) with, for example, logistic regression. A marker is considered useful if it has a strong effect on risk. The second evaluates classification performance by use of measures such a...

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Vydané v:	American journal of epidemiology Ročník 167; číslo 3; s. 362
Hlavní autori:	Pepe, Margaret S, Feng, Ziding, Huang, Ying, Longton, Gary, Prentice, Ross, Thompson, Ian M, Zheng, Yingye
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States 01.02.2008
Predmet:	Biomarkers - analysis Epidemiologic Methods Humans Logistic Models Male Models, Theoretical Predictive Value of Tests Prostate-Specific Antigen - blood Risk Risk Assessment - methods ROC Curve Sensitivity and Specificity
ISSN:	1476-6256, 1476-6256
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Abstract	There are two popular statistical approaches to biomarker evaluation. One models the risk of disease (or disease outcome) with, for example, logistic regression. A marker is considered useful if it has a strong effect on risk. The second evaluates classification performance by use of measures such as sensitivity, specificity, predictive values, and receiver operating characteristic curves. There is controversy about which approach is more appropriate. Moreover, the two approaches can give contradictory results on the same data. The authors present a new graphic, the predictiveness curve, which complements the risk modeling approach. It assesses the usefulness of a risk model when applied to the population. Although the predictiveness curve relates to classification performance measures, it also displays essential information about risk that is not displayed by the receiver operating characteristic curve. The authors propose that the predictiveness and classification performance of a marker, displayed together in an integrated plot, provide a comprehensive and cohesive assessment of a risk marker or model. The methods are demonstrated with data on prostate-specific antigen and risk factors from the Prostate Cancer Prevention Trial, 1993-2003.
AbstractList	There are two popular statistical approaches to biomarker evaluation. One models the risk of disease (or disease outcome) with, for example, logistic regression. A marker is considered useful if it has a strong effect on risk. The second evaluates classification performance by use of measures such as sensitivity, specificity, predictive values, and receiver operating characteristic curves. There is controversy about which approach is more appropriate. Moreover, the two approaches can give contradictory results on the same data. The authors present a new graphic, the predictiveness curve, which complements the risk modeling approach. It assesses the usefulness of a risk model when applied to the population. Although the predictiveness curve relates to classification performance measures, it also displays essential information about risk that is not displayed by the receiver operating characteristic curve. The authors propose that the predictiveness and classification performance of a marker, displayed together in an integrated plot, provide a comprehensive and cohesive assessment of a risk marker or model. The methods are demonstrated with data on prostate-specific antigen and risk factors from the Prostate Cancer Prevention Trial, 1993-2003. There are two popular statistical approaches to biomarker evaluation. One models the risk of disease (or disease outcome) with, for example, logistic regression. A marker is considered useful if it has a strong effect on risk. The second evaluates classification performance by use of measures such as sensitivity, specificity, predictive values, and receiver operating characteristic curves. There is controversy about which approach is more appropriate. Moreover, the two approaches can give contradictory results on the same data. The authors present a new graphic, the predictiveness curve, which complements the risk modeling approach. It assesses the usefulness of a risk model when applied to the population. Although the predictiveness curve relates to classification performance measures, it also displays essential information about risk that is not displayed by the receiver operating characteristic curve. The authors propose that the predictiveness and classification performance of a marker, displayed together in an integrated plot, provide a comprehensive and cohesive assessment of a risk marker or model. The methods are demonstrated with data on prostate-specific antigen and risk factors from the Prostate Cancer Prevention Trial, 1993-2003.There are two popular statistical approaches to biomarker evaluation. One models the risk of disease (or disease outcome) with, for example, logistic regression. A marker is considered useful if it has a strong effect on risk. The second evaluates classification performance by use of measures such as sensitivity, specificity, predictive values, and receiver operating characteristic curves. There is controversy about which approach is more appropriate. Moreover, the two approaches can give contradictory results on the same data. The authors present a new graphic, the predictiveness curve, which complements the risk modeling approach. It assesses the usefulness of a risk model when applied to the population. Although the predictiveness curve relates to classification performance measures, it also displays essential information about risk that is not displayed by the receiver operating characteristic curve. The authors propose that the predictiveness and classification performance of a marker, displayed together in an integrated plot, provide a comprehensive and cohesive assessment of a risk marker or model. The methods are demonstrated with data on prostate-specific antigen and risk factors from the Prostate Cancer Prevention Trial, 1993-2003.
Author	Feng, Ziding Longton, Gary Prentice, Ross Zheng, Yingye Huang, Ying Pepe, Margaret S Thompson, Ian M
Author_xml	– sequence: 1 givenname: Margaret S surname: Pepe fullname: Pepe, Margaret S email: mspepe@u.washington.edu organization: Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA. mspepe@u.washington.edu – sequence: 2 givenname: Ziding surname: Feng fullname: Feng, Ziding – sequence: 3 givenname: Ying surname: Huang fullname: Huang, Ying – sequence: 4 givenname: Gary surname: Longton fullname: Longton, Gary – sequence: 5 givenname: Ross surname: Prentice fullname: Prentice, Ross – sequence: 6 givenname: Ian M surname: Thompson fullname: Thompson, Ian M – sequence: 7 givenname: Yingye surname: Zheng fullname: Zheng, Yingye
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/17982157$$D View this record in MEDLINE/PubMed
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References	12230001 - Biometrics. 2002 Sep;58(3):657-64 16622122 - J Natl Cancer Inst. 2006 Apr 19;98(8):529-34 12644540 - J Natl Cancer Inst. 2003 Mar 19;95(6):470-8 7055134 - Am J Epidemiol. 1982 Jan;115(1):92-106 7662848 - Biometrics. 1995 Jun;51(2):600-14 12809422 - Acad Radiol. 2003 Jun;10(6):670-2 11890304 - Biometrics. 2002 Mar;58(1):1-12 17489968 - Biometrics. 2007 Dec;63(4):1181-8 15105181 - Am J Epidemiol. 2004 May 1;159(9):882-90 17679623 - Circulation. 2007 Aug 7;116(6):e132; author reply e134 11782052 - Stat Med. 2002 Jan 15;21(1):79-93 15772102 - Biostatistics. 2005 Apr;6(2):227-39 7127706 - Circulation. 1982 Nov;66(5):945-53 8896134 - Stat Med. 1996 Oct 15;15(19):1987-97 17569110 - Stat Med. 2008 Jan 30;27(2):157-72; discussion 207-12 16818925 - Ann Intern Med. 2006 Jul 4;145(1):21-9
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Snippet	There are two popular statistical approaches to biomarker evaluation. One models the risk of disease (or disease outcome) with, for example, logistic...
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SubjectTerms	Biomarkers - analysis Epidemiologic Methods Humans Logistic Models Male Models, Theoretical Predictive Value of Tests Prostate-Specific Antigen - blood Risk Risk Assessment - methods ROC Curve Sensitivity and Specificity
Title	Integrating the predictiveness of a marker with its performance as a classifier
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