Abstract
Statistical constructs such as ROC/AUC do not answer the critical question of how much clinical utility a risk prediction model confers. This paper overviews decision curve analysis, a novel method for quantifying net benefit of a risk prediction model. https://bit.ly/3h1rraX
Footnotes
Conflict of interest: M. Sadatsafavi reports grants and personal fees from Boehringer Ingelheim and AstraZeneca, personal fees from GlaxoSmithKline and Teva, outside the submitted work.
Conflict of interest: A. Adibi has nothing to disclose.
Conflict of interest: M. Puhan has nothing to disclose.
Conflict of interest: A. Gershon has nothing to disclose.
Conflict of interest: S.D. Aaron has nothing to disclose.
Conflict of interest: D.D. Sin reports grants and personal fees for advisory board and educational work from AstraZeneca, personal fees for lectures from Boehringer Ingelheim, personal fees for advisory board work from Grifols, outside the submitted work.
Support statement: This work was supported by the BC SUPPORT Unit Methods Cluster Project Award, grant HESM 205. Funding information for this article has been deposited with the Crossref Funder Registry.
- Received April 26, 2021.
- Accepted August 31, 2021.
- Copyright ©The authors 2021. For reproduction rights and permissions contact permissions{at}ersnet.org
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