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Quantifying discrimination of Framingham risk functions with different survival C statistics

Pencina, Michael J. ; D'Agostino Sr, Ralph B. ; Song, Linye

Statistics in medicine, 2012-07, Vol.31 (15), p.1543-1553 [Periódico revisado por pares]

Chichester, UK: John Wiley & Sons, Ltd

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Título:
Quantifying discrimination of Framingham risk functions with different survival C statistics
Autor: Pencina, Michael J. ; D'Agostino Sr, Ralph B. ; Song, Linye
Assuntos: AUC ; Cardiovascular Diseases - epidemiology ; Cardiovascular Diseases - physiopathology ; censoring ; Cohort Studies ; concordance ; Data Interpretation, Statistical ; discrimination ; Female ; Humans ; Kaplan-Meier Estimate ; Male ; Mathematical functions ; Mathematical models ; Performance evaluation ; Prognosis ; Proportional Hazards Models ; Regression analysis ; Risk Assessment - methods ; Risk Assessment - statistics & numerical data ; risk function ; ROC Curve ; Statistics ; Survival Analysis
É parte de: Statistics in medicine, 2012-07, Vol.31 (15), p.1543-1553
Notas: ark:/67375/WNG-SCJ8MR9L-J
ArticleID:SIM4508
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ObjectType-Article-1
SourceType-Scholarly Journals-1
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Descrição: Cardiovascular risk prediction functions offer an important diagnostic tool for clinicians and patients themselves. They are usually constructed with the use of parametric or semi‐parametric survival regression models. It is essential to be able to evaluate the performance of these models, preferably with summaries that offer natural and intuitive interpretations. The concept of discrimination, popular in the logistic regression context, has been extended to survival analysis. However, the extension is not unique. In this paper, we define discrimination in survival analysis as the model's ability to separate those with longer event‐free survival from those with shorter event‐free survival within some time horizon of interest. This definition remains consistent with that used in logistic regression, in the sense that it assesses how well the model‐based predictions match the observed data. Practical and conceptual examples and numerical simulations are employed to examine four C statistics proposed in the literature to evaluate the performance of survival models. We observe that they differ in the numerical values and aspects of discrimination that they capture. We conclude that the index proposed by Harrell is the most appropriate to capture discrimination described by the above definition. We suggest researchers report which C statistic they are using, provide a rationale for their selection, and be aware that comparing different indices across studies may not be meaningful. Copyright © 2012 John Wiley & Sons, Ltd.
Editor: Chichester, UK: John Wiley & Sons, Ltd
Idioma: Inglês

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Quantifying discrimination of Framingham risk functions with different survival C statistics

Pencina, Michael J. ; D'Agostino Sr, Ralph B. ; Song, Linye

Chichester, UK: John Wiley & Sons, Ltd

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