Reporting and interpreting decision curve analysis: A guide for investigators Journal Article


Authors: Van Calster, B.; Wynants, L.; Verbeek, J. F. M.; Verbakel, J. Y.; Christodoulou, E.; Vickers, A. J.; Roobol, M. J.; Steyerberg, E. W.
Article Title: Reporting and interpreting decision curve analysis: A guide for investigators
Abstract: Context: Urologists regularly develop clinical risk prediction models to support clinical decisions. In contrast to traditional performance measures, decision curve analysis (DCA) can assess the utility of models for decision making. DCA plots net benefit (NB) at a range of clinically reasonable risk thresholds. Objective: To provide recommendations on interpreting and reporting DCA when evaluating prediction models. Evidence acquisition: We informally reviewed the urological literature to determine investigators’ understanding of DCA. To illustrate, we use data from 3616 patients to develop risk models for high-grade prostate cancer (n = 313, 9%) to decide who should undergo a biopsy. The baseline model includes prostate-specific antigen and digital rectal examination; the extended model adds two predictors based on transrectal ultrasound (TRUS). Evidence synthesis: We explain risk thresholds, NB, default strategies (treat all, treat no one), and test tradeoff. To use DCA, first determine whether a model is superior to all other strategies across the range of reasonable risk thresholds. If so, that model appears to improve decisions irrespective of threshold. Second, consider if there are important extra costs to using the model. If so, obtain the test tradeoff to check whether the increase in NB versus the best other strategy is worth the additional cost. In our case study, addition of TRUS improved NB by 0.0114, equivalent to 1.1 more detected high-grade prostate cancers per 100 patients. Hence, adding TRUS would be worthwhile if we accept subjecting 88 patients to TRUS to find one additional high-grade prostate cancer or, alternatively, subjecting 10 patients to TRUS to avoid one unnecessary biopsy. Conclusions: The proposed guidelines can help researchers understand DCA and improve application and reporting. Patient summary: Decision curve analysis can identify risk models that can help us make better clinical decisions. We illustrate appropriate reporting and interpretation of decision curve analysis. Decision curve analysis (DCA) is a method to evaluate whether risk prediction models can have utility for supporting treatment decisions. This guide for researchers explains what DCA is, how to interpret it, and how to report its results. © 2018 European Association of Urology
Keywords: controlled study; human tissue; major clinical study; review; cancer risk; cancer diagnosis; prostate specific antigen; prediction; prostate cancer; prostate biopsy; clinical decision making; false positive result; transrectal ultrasonography; clinical utility; digital rectal examination; decision curve analysis; prostate volume; net benefit; human; male; priority journal; risk prediction models; risk threshold; test tradeoff
Journal Title: European Urology
Volume: 74
Issue: 6
ISSN: 0302-2838
Publisher: Elsevier Science, Inc.  
Date Published: 2018-12-01
Start Page: 796
End Page: 804
Language: English
DOI: 10.1016/j.eururo.2018.08.038
PUBMED: 30241973
PROVIDER: scopus
PMCID: PMC6261531
DOI/URL:
Notes: Review -- Export Date: 3 December 2018 -- Source: Scopus
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  1. Andrew J Vickers
    556 Vickers