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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

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

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:

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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