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Diagn Progn Res. 2019 Oct 4;3:18. doi: 10.1186/s41512-019-0064-7. eCollection 2019.

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

Author information

1Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 485 Lexington Avenue, 2nd Floor, New York, NY 10017 USA.
2Department of Development and Regeneration, KU Leuven, Oude Markt 13, 3000 Leuven, Belgium.
3Department of Biomedical Data Sciences, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, Netherlands.



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.


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


Decision curve analysis; Educational paper; Net benefit

Conflict of interest statement

Competing interestsThe authors declare that they have no competing interests.

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