Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators

By B. Van calster, Laure Wynants, J. Verbeek, J. Verbakel, Evangelia Christodoulou, Andrew J. Vickers, M. Roobol, Ewout W. Steyerberg. 2018

1,022 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Pulmonary and Respiratory Medicine

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Citations in the corpus

Developing an optimal short-form of the PTSD Checklist for DSM-5 (PCL-5) (2019, background): “…scales, NB provides more intuitive and clinically useful information than SN, SP, and PPV in comparing scales because it accounts for between‐ clinician variation in the relative valuations of correctly detecting a true positive and correctly excluding a true negative (Van Calster et al., 2018).”
A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. (2019, methodology): “Clinical utility can be assessed using decision curve analysis, which is increasingly used in medical applications [100].”