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

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.