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Med Decis Making. 2019 Feb 28:272989X19832881. doi: 10.1177/0272989X19832881. [Epub ahead of print]

A Systematic Review of the Literature Demonstrates Some Errors in the Use of Decision Curve Analysis but Generally Correct Interpretation of Findings.

Author information

1
Università Vita-Salute San Raffaele, Milan, Italy.
2
Division of Experimental Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy.
3
Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Abstract

BACKGROUND:

Decision curve analysis (DCA) is a widely used methodology in clinical research studies.

PURPOSE:

We performed a literature review to identify common errors in the application of DCA and provide practical suggestions for appropriate use of DCA.

DATA SOURCES:

We first conducted an informal literature review and identified 6 errors found in some DCAs. We then used Google Scholar to conduct a systematic review of studies applying DCA to evaluate a predictive model, marker, or test.

DATA EXTRACTION:

We used a standard data collection form to collect data for each reviewed article.

DATA SYNTHESIS:

Each article was assessed according to the 6 predefined criteria for a proper analysis, reporting, and interpretation of DCA. Overall, 50 articles were included in the review: 54% did not select an appropriate range of probability thresholds for the x-axis of the DCA, with a similar proportion (50%) failing to present smoothed curves. Among studies with internal validation of a predictive model and correction for overfit, 61% did not clearly report whether the DCA had also been corrected. However, almost all studies correctly interpreted the DCA, used a correct outcome (92% for both), and clearly reported the clinical decision at issue (81%).

LIMITATIONS:

A comprehensive assessment of all DCAs was not performed. However, such a strategy would not influence the main findings.

CONCLUSIONS:

Despite some common errors in the application of DCA, our finding that almost all studies correctly interpreted the DCA results demonstrates that it is a clear and intuitive method to assess clinical utility.

KEYWORDS:

decision curve analysis; prediction; quality

PMID:
30819037
DOI:
10.1177/0272989X19832881

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