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Understanding increments in model performance metrics.

Publication ,  Journal Article
Pencina, MJ; D'Agostino, RB; Massaro, JM
Published in: Lifetime Data Anal
April 2013

The area under the receiver operating characteristic curve (AUC) is the most commonly reported measure of discrimination for prediction models with binary outcomes. However, recently it has been criticized for its inability to increase when important risk factors are added to a baseline model with good discrimination. This has led to the claim that the reliance on the AUC as a measure of discrimination may miss important improvements in clinical performance of risk prediction rules derived from a baseline model. In this paper we investigate this claim by relating the AUC to measures of clinical performance based on sensitivity and specificity under the assumption of multivariate normality. The behavior of the AUC is contrasted with that of discrimination slope. We show that unless rules with very good specificity are desired, the change in the AUC does an adequate job as a predictor of the change in measures of clinical performance. However, stronger or more numerous predictors are needed to achieve the same increment in the AUC for baseline models with good versus poor discrimination. When excellent specificity is desired, our results suggest that the discrimination slope might be a better measure of model improvement than AUC. The theoretical results are illustrated using a Framingham Heart Study example of a model for predicting the 10-year incidence of atrial fibrillation.

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Published In

Lifetime Data Anal

DOI

EISSN

1572-9249

Publication Date

April 2013

Volume

19

Issue

2

Start / End Page

202 / 218

Location

United States

Related Subject Headings

  • Statistics & Probability
  • Risk Assessment
  • Prospective Studies
  • Models, Statistical
  • Middle Aged
  • Male
  • Humans
  • Female
  • Discriminant Analysis
  • Cardiovascular Diseases
 

Citation

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Pencina, M. J., D’Agostino, R. B., & Massaro, J. M. (2013). Understanding increments in model performance metrics. Lifetime Data Anal, 19(2), 202–218. https://doi.org/10.1007/s10985-012-9238-0
Pencina, Michael J., Ralph B. D’Agostino, and Joseph M. Massaro. “Understanding increments in model performance metrics.Lifetime Data Anal 19, no. 2 (April 2013): 202–18. https://doi.org/10.1007/s10985-012-9238-0.
Pencina MJ, D’Agostino RB, Massaro JM. Understanding increments in model performance metrics. Lifetime Data Anal. 2013 Apr;19(2):202–18.
Pencina, Michael J., et al. “Understanding increments in model performance metrics.Lifetime Data Anal, vol. 19, no. 2, Apr. 2013, pp. 202–18. Pubmed, doi:10.1007/s10985-012-9238-0.
Pencina MJ, D’Agostino RB, Massaro JM. Understanding increments in model performance metrics. Lifetime Data Anal. 2013 Apr;19(2):202–218.
Journal cover image

Published In

Lifetime Data Anal

DOI

EISSN

1572-9249

Publication Date

April 2013

Volume

19

Issue

2

Start / End Page

202 / 218

Location

United States

Related Subject Headings

  • Statistics & Probability
  • Risk Assessment
  • Prospective Studies
  • Models, Statistical
  • Middle Aged
  • Male
  • Humans
  • Female
  • Discriminant Analysis
  • Cardiovascular Diseases