A simple, step-by-step guide to interpreting decision curve analysis

By Andrew J. Vickers, B. Van calster, Ewout W. Steyerberg. Diagnostic and Prognostic Research, 2019

915 citations recorded by the source corpus.

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. 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. Decision curves are readily interpretable if readers and authors follow a few simple guidelines.

Subfields: Economics and Econometrics, General Health Professions, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8c31adfbedb659bf7efed45003e6e93f72231051

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