Using the weighted area under the net benefit curve for decision curve analysis.

ABSTRACT:

Background

Risk prediction models have been proposed for various diseases and are being improved as new predictors are identified. A major challenge is to determine whether the newly discovered predictors improve risk prediction. Decision curve analysis has been proposed as an alternative to the area under the curve and net reclassification index to evaluate the performance of prediction models in clinical scenarios. The decision curve computed using the net benefit can evaluate the predictive performance of risk models at a given or range of threshold probabilities. However, when the decision curves for 2 competing models cross in the range of interest, it is difficult to identify the best model as there is no readily available summary measure for evaluating the predictive perform

SUBMITTER: Talluri R 

PROVIDER: S-EPMC4949771 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature