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

Author

Listed:
  • Paolo Capogrosso

    (Università Vita-Salute San Raffaele, Milan, Italy
    Division of Experimental Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy)

  • Andrew J. Vickers

    (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.

Suggested Citation

  • Paolo Capogrosso & Andrew J. Vickers, 2019. "A Systematic Review of the Literature Demonstrates Some Errors in the Use of Decision Curve Analysis but Generally Correct Interpretation of Findings," Medical Decision Making, , vol. 39(5), pages 493-498, July.
  • Handle: RePEc:sae:medema:v:39:y:2019:i:5:p:493-498
    DOI: 10.1177/0272989X19832881
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    References listed on IDEAS

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    1. Lars Holmberg & Andrew Vickers, 2013. "Evaluation of Prediction Models for Decision-Making: Beyond Calibration and Discrimination," PLOS Medicine, Public Library of Science, vol. 10(7), pages 1-2, July.
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    Cited by:

    1. Stuart G. Baker, 2019. "Decision Curves and Relative Utility Curves," Medical Decision Making, , vol. 39(5), pages 489-490, July.
    2. Kathleen F. Kerr & Tracey L. Marsh & Holly Janes, 2019. "The Importance of Uncertainty and Opt-In v. Opt-Out: Best Practices for Decision Curve Analysis," Medical Decision Making, , vol. 39(5), pages 491-492, July.
    3. Yuan Gao & Sofia Ventura-Diaz & Xin Wang & Muzhen He & Zeyan Xu & Arlene Weir & Hong-Yu Zhou & Tianyu Zhang & Frederieke H. Duijnhoven & Luyi Han & Xiaomei Li & Anna D’Angelo & Valentina Longo & Zaiyi, 2024. "An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer," Nature Communications, Nature, vol. 15(1), pages 1-17, December.

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