Assessing the performance of prediction models: a framework for traditional and novel measures.


Journal Article

The performance of prediction models can be assessed using a variety of methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic [ROC] curve), and goodness-of-fit statistics for calibration.Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision-analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions.We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration, we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n = 544 for model development, n = 273 for external validation).We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.

Full Text

Duke Authors

Cited Authors

  • Steyerberg, EW; Vickers, AJ; Cook, NR; Gerds, T; Gonen, M; Obuchowski, N; Pencina, MJ; Kattan, MW

Published Date

  • January 2010

Published In

Volume / Issue

  • 21 / 1

Start / End Page

  • 128 - 138

PubMed ID

  • 20010215

Pubmed Central ID

  • 20010215

Electronic International Standard Serial Number (EISSN)

  • 1531-5487

Digital Object Identifier (DOI)

  • 10.1097/EDE.0b013e3181c30fb2


  • eng

Conference Location

  • United States