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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Vydáno v:American journal of epidemiology Ročník 167; číslo 3; s. 362
Hlavní autoři: Pepe, Margaret S, Feng, Ziding, Huang, Ying, Longton, Gary, Prentice, Ross, Thompson, Ian M, Zheng, Yingye
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 01.02.2008
Témata:
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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Shrnutí: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.
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ISSN:1476-6256
1476-6256
DOI:10.1093/aje/kwm305