Assessing the Performance of Prediction Models

TLDR

Prediction model performance is evaluated with a range of metrics, from traditional Brier scores and c‑statistics to newer discrimination refinements and decision‑analytic curves that assess net benefit. The study aims to clarify how novel discrimination and decision‑analytic measures contribute to evaluating prediction model performance. The authors illustrate their approach with a testicular cancer case study, developing a model on 544 patients and validating it on 273, and emphasize that discrimination and calibration reporting is essential. They recommend reporting discrimination and calibration, including decision‑analytic measures for clinical use, and consider reclassification metrics when adding new predictors.

Abstract

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.