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Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond

Pencina, Michael J. ; D' Agostino Sr, Ralph B. ; D' Agostino Jr, Ralph B. ; Vasan, Ramachandran S.

Statistics in medicine, 2008-01, Vol.27 (2), p.157-172 [Periódico revisado por pares]

Chichester, UK: John Wiley & Sons, Ltd

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  • Título:
    Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond
  • Autor: Pencina, Michael J. ; D' Agostino Sr, Ralph B. ; D' Agostino Jr, Ralph B. ; Vasan, Ramachandran S.
  • Assuntos: Algorithms ; Area Under Curve ; AUC ; biomarker ; Cardiology ; Cardiovascular disease ; Cardiovascular Diseases - etiology ; discrimination ; Epidemiology ; Humans ; model performance ; Models, Statistical ; Performance evaluation ; Risk Assessment - classification ; Risk Assessment - statistics & numerical data ; Risk Factors ; risk prediction ; ROC Curve ; United States
  • É parte de: Statistics in medicine, 2008-01, Vol.27 (2), p.157-172
  • Notas: istex:13F8CBF61345B3F702CDD38C802DB8A2752D2F1D
    National Heart, Lung, and Blood Institute's Framingham Heart Study - No. N01-HC-25195; No. 2K24 HL 04334
    ArticleID:SIM2929
    ark:/67375/WNG-KGR46L70-T
    ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Descrição: Identification of key factors associated with the risk of developing cardiovascular disease and quantification of this risk using multivariable prediction algorithms are among the major advances made in preventive cardiology and cardiovascular epidemiology in the 20th century. The ongoing discovery of new risk markers by scientists presents opportunities and challenges for statisticians and clinicians to evaluate these biomarkers and to develop new risk formulations that incorporate them. One of the key questions is how best to assess and quantify the improvement in risk prediction offered by these new models. Demonstration of a statistically significant association of a new biomarker with cardiovascular risk is not enough. Some researchers have advanced that the improvement in the area under the receiver‐operating‐characteristic curve (AUC) should be the main criterion, whereas others argue that better measures of performance of prediction models are needed. In this paper, we address this question by introducing two new measures, one based on integrated sensitivity and specificity and the other on reclassification tables. These new measures offer incremental information over the AUC. We discuss the properties of these new measures and contrast them with the AUC. We also develop simple asymptotic tests of significance. We illustrate the use of these measures with an example from the Framingham Heart Study. We propose that scientists consider these types of measures in addition to the AUC when assessing the performance of newer biomarkers. Copyright © 2007 John Wiley & Sons, Ltd.
  • Editor: Chichester, UK: John Wiley & Sons, Ltd
  • Idioma: Inglês
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