# Decision curve analysis: a technical note.

## Détails

ID Serval

serval:BIB_DA7BA08F1A6E

Type

**Article**: article d'un périodique ou d'un magazine.

Sous-type

**Editorial**

Collection

Publications

Institution

Titre

Decision curve analysis: a technical note.

Périodique

Annals of translational medicine

Collaborateur⸱rice⸱s

written on behalf of AME Big-Data Clinical Trial Collaborative Group

ISSN

2305-5839 (Print)

ISSN-L

2305-5839

Statut éditorial

Publié

Date de publication

08/2018

Peer-reviewed

Oui

Volume

6

Numéro

15

Pages

308

Langue

anglais

Notes

Publication types: Editorial

Publication Status: ppublish

Publication Status: ppublish

Résumé

Multivariable regression models are widely used in medical literature for the purpose of diagnosis or prediction. Conventionally, the adequacy of these models is assessed using metrics of diagnostic performances such as sensitivity and specificity, which fail to account for clinical utility of a specific model. Decision curve analysis (DCA) is a widely used method to measure this utility. In this framework, a clinical judgment of the relative value of benefits (treating a true positive case) and harms (treating a false positive case) associated with prediction models is made. As such, the preferences of patients or policy-makers are accounted for by using a metric called threshold probability. A decision analytic measure called net benefit is then calculated for each possible threshold probability, which puts benefits and harms on the same scale. The article is a technical note on how to perform DCA in R environment. The decision curve is depicted with the <i>ggplot2</i> system. Correction for overfitting is done via either bootstrap or cross-validation. Confidence interval and P values for the comparison of two models are calculated using bootstrap method. Furthermore, we describe a method for computing area under net benefit for the comparison of two models. The average deviation about the probability threshold (ADAPT), which is a more recently developed index to measure the utility of a prediction model, is also introduced in this article.

Mots-clé

Decision curve analysis (DCA), diagnostic test, outcome, prediction model

Pubmed

Web of science

Création de la notice

19/09/2018 12:38

Dernière modification de la notice

20/08/2019 15:59