• AUC;
  • Brier score;
  • 0.632+ Bootstrap;
  • Crossvalidation;
  • Machine learning;
  • Model selection;
  • R2;
  • Risk prediction;
  • ROC curve


For medical decision making and patient information, predictions of future status variables play an important role. Risk prediction models can be derived with many different statistical approaches. To compare them, measures of predictive performance are derived from ROC methodology and from probability forecasting theory. These tools can be applied to assess single markers, multivariable regression models and complex model selection algorithms. This article provides a systematic review of the modern way of assessing risk prediction models. Particular attention is put on proper benchmarks and resampling techniques that are important for the interpretation of measured performance. All methods are illustrated with data from a clinical study in head and neck cancer patients. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)