Decision Curve Analysis

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A Simple Method for Evaluating Prediction Models, Diagnostic Tests, and Molecular Markers

Diagnostic & prognostic models are evaluated by accuracy measures (e.g. AUC) that don’t address clinical consequences. Decision-analytic techniques address those consequences, but only with extensive information, and are not easily applicable to models with percent risk estimates. DCA incorporates clinical consequences, addressing model benefits and harms with limited data.

 

Code (R, Python, Stata, and SAS)
Tutorial & Walkthrough (R, Python, Stata, and SAS)
Other Resources
Peer-Reviewed Literature

Original Paper on DCA

Extensions to DCA

Introductory Papers and Guides

Discussion Papers and Theoretical Background

Editorials and Commentaries Recommending DCA