Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers Journal Article
| Authors: | Vickers, A. J.; Cronin, A. M.; Elkin, E. B.; Gonen, M. |
| Article Title: | Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers |
| Abstract: | Background. Decision curve analysis is a novel method for evaluating diagnostic tests, prediction models and molecular markers. It combines the mathematical simplicity of accuracy measures, such as sensitivity and specificity, with the clinical applicability of decision analytic approaches. Most critically, decision curve analysis can be applied directly to a data set, and does not require the sort of external data on costs, benefits and preferences typically required by traditional decision analytic techniques. Methods. In this paper we present several extensions to decision curve analysis including correction for overfit, confidence intervals, application to censored data (including competing risk) and calculation of decision curves directly from predicted probabilities. All of these extensions are based on straightforward methods that have previously been described in the literature for application to analogous statistical techniques. Results. Simulation studies showed that repeated 10-fold crossvalidation provided the best method for correcting a decision curve for overfit. The method for applying decision curves to censored data had little bias and coverage was excellent; for competing risk, decision curves were appropriately affected by the incidence of the competing risk and the association between the competing risk and the predictor of interest. Calculation of decision curves directly from predicted probabilities led to a smoothing of the decision curve. Conclusion. Decision curve analysis can be easily extended to many of the applications common to performance measures for prediction models. Software to implement decision curve analysis is provided. © 2008 Vickers et al; licensee BioMed Central Ltd. |
| Keywords: | controlled study; validation process; medical decision making; biological marker; biological markers; logistic models; prediction; risk assessment; mathematical model; confidence interval; simulation; confidence intervals; prostatic neoplasms; molecular marker; statistical analysis; probability; prostate tumor; prediction and forecasting; predictive value of tests; computer simulation; diagnostic test; molecular biology; statistical model; decision support techniques; decision support system; decision curve analysis; decision support systems, clinical; diagnostic tests, routine |
| Journal Title: | BMC Medical Informatics and Decision Making |
| Volume: | 8 |
| ISSN: | 1472-6947 |
| Publisher: | Biomed Central Ltd |
| Date Published: | 2008-11-26 |
| Start Page: | 53 |
| Language: | English |
| DOI: | 10.1186/1472-6947-8-53 |
| PUBMED: | 19036144 |
| PROVIDER: | scopus |
| PMCID: | PMC2611975 |
| DOI/URL: | |
| Notes: | --- - "Cited By (since 1996): 17" - "Export Date: 17 November 2011" - "Source: Scopus" |
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