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 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.
Pepe
Margaret S
MS
Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA. mspepe@u.washington.edu
Feng
Ziding
Z
Huang
Ying
Y
Longton
Gary
G
Prentice
Ross
R
Thompson
Ian M
IM
Zheng
Yingye
Y
D015415:Biomarkers / Q000032:analysis*; D004812:Epidemiologic Methods; D006801:Humans; D016015:Logistic Models; D008297:Male; D008962:Models, Theoretical; D011237:Predictive Value of Tests; D017430:Prostate-Specific Antigen / Q000097:blood; D012372:ROC Curve; D012306:Risk; D018570:Risk Assessment / Q000379:methods*; D012680:Sensitivity and Specificity
D015415:Biomarkers; D017430:Prostate-Specific Antigen