A simple, step-by-step guide to interpreting decision curve analysis
Top Cited Papers
Open Access
- 4 October 2019
- journal article
- editorial
- Published by Springer Science and Business Media LLC in Diagnostic and Prognostic Research
- Vol. 3 (1), 1-8
- https://doi.org/10.1186/s41512-019-0064-7
Abstract
Background: Decision curve analysis is a method to evaluate prediction models and diagnostic tests that was introduced in a 2006 publication. Decision curves are now commonly reported in the literature, but there remains widespread misunderstanding of and confusion about what they mean. Summary of commentary: In this paper, we present a didactic, step-by-step introduction to interpreting a decision curve analysis and answer some common questions about the method. We argue that many of the difficulties with interpreting decision curves can be solved by relabeling the y-axis as “benefit” and the x-axis as “preference.” A model or test can be recommended for clinical use if it has the highest level of benefit across a range of clinically reasonable preferences. Conclusion: Decision curves are readily interpretable if readers and authors follow a few simple guidelines.Keywords
Funding Information
- National Cancer Institute (P50-CA92629)
- National Institutes of Health (P30-CA008748, U01 NS086294)
This publication has 14 references indexed in Scilit:
- Calibration of Risk Prediction ModelsMedical Decision Making, 2014
- Evaluation of Prediction Models for Decision-Making: Beyond Calibration and DiscriminationPLoS Medicine, 2013
- Beyond the Usual Prediction Accuracy Metrics: Reporting Results for Clinical Decision MakingAnnals of Internal Medicine, 2012
- Prospective Multi-Institutional Study Evaluating the Performance of Prostate Cancer Risk CalculatorsJournal of Clinical Oncology, 2011
- Using Relative Utility Curves to Evaluate Risk PredictionJournal of the Royal Statistical Society Series A: Statistics in Society, 2009
- Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markersBMC Medical Informatics and Decision Making, 2008
- Method for evaluating prediction models that apply the results of randomized trials to individual patientsTrials, 2007
- Decision Curve Analysis: A Novel Method for Evaluating Prediction ModelsMedical Decision Making, 2006
- Net Health BenefitsMedical Decision Making, 1998
- The Threshold Approach to Clinical Decision MakingThe New England Journal of Medicine, 1980