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. 2008 Nov 26:8:53.
doi: 10.1186/1472-6947-8-53.

Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers

Andrew J Vickers  1 , Angel M Cronin, Elena B Elkin, Mithat Gonen
Affiliations

V体育官网入口 - Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers

Andrew J Vickers et al. BMC Med Inform Decis Mak. .

Abstract

Background: Decision curve analysis is a novel method for evaluating diagnostic tests, prediction models and molecular markers VSports手机版. 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 V体育安卓版. .

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. V体育ios版.

Conclusion: Decision curve analysis can be easily extended to many of the applications common to performance measures for prediction models VSports最新版本. Software to implement decision curve analysis is provided. .

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Figures (V体育平台登录)

Figure 1
Figure 1
Decision curve for a model predicting the outcome of prostate biopsy. The thin grey line is the net benefit of biopsying all men; the thin black line is the net benefit of biopsying men on the basis of the statistical model; the thick black line is the net benefit of biopsying no man.
Figure 2
Figure 2
Decision curve for a model predicting the outcome of prostate biopsy. The thin grey line is the net benefit of biopsying all men; the thin black line is the net benefit of biopsying men on the basis of the statistical model; the thick black line is the net benefit of biopsying no man. The decision curve is shown for the key threshold probability range 10 – 40%.
Figure 3
Figure 3
Decision curve for a model predicting the outcome of prostate biopsy, with correction for overfit by crossvalidation. The thin grey line is the net benefit of biopsying all men; the dashed black line is the net benefit of biopsying men on the basis of the statistical model; the thin black line is the results of the statistical model corrected for overfit; the thick black line is the net benefit of biopsying no man.
Figure 4
Figure 4
Decision curve for a model predicting the outcome of prostate biopsy, with correction for overfit by bootstrap. The thin grey line is the net benefit of biopsying all men; the dashed black line is the net benefit of biopsying men on the basis of the statistical model; the thin black line is the results of the statistical model corrected for overfit; the thick black line is the net benefit of biopsying no man.
Figure 5
Figure 5
Decision curves for survival time data. The thick grey line is the net benefit for a strategy of treating all men; the thick black line is the net benefit of treating no men. A thin grey line is calculated from an uncensored data set for a binary variable of survival at time t; a thin black line is calculated from the data set after censoring was introduced, using the net benefit formula for censored data. The two curves are essentially overlapping and appear as a single dark grey line.
Figure 6
Figure 6
Decision curve for survival time data with and without adjustment for competing risk, where the incidence of competing risks is high (bladder cancer data set). The thick grey line is the net benefit for a strategy of treating all patients with (dashed line) and without (solid line) adjustment for competing risk; the thin black line is the net benefit of a strategy of treating patients according to the model with (dashed line) and without (solid line) adjustment for competing risk; the thick black line is the net benefit of treating no patients.
Figure 7
Figure 7
Decision curve for survival time data with and without adjustment for competing risk, where the incidence of competing risks is low (prostate cancer data set). The thick grey line is the net benefit for a strategy of treating all men with (dashed line) and without (solid line) adjustment for competing risk; the thin black line is the net benefit of a strategy of treating men according to the model with (dashed line) and without (solid line) adjustment for competing risk; the thick black line is the net benefit of treating no men. Since the incidence of competing risk is low, the curves for treating all are essentially overlapping and appear as a single grey line.
Figure 8
Figure 8
Decision curve for complete data set calculated directly from predicted probabilities.
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