Decision curves are a useful tool to evaluate the population impact of adopting a risk prediction instrument into clinical practice. Given one or more instruments (risk models) that predict the probability of a binary outcome, this package calculates and plots decision curves, which display estimates of the standardized net benefit by the probability threshold used to categorize observations as 'high risk.' Curves can be estimated using data from an observational cohort, or from case-control studies when an estimate of the population outcome prevalence is available. Confidence intervals calculated using the bootstrap can be displayed and a wrapper function to calculate cross-validated curves using k-fold cross-validation is also provided.
|Date of publication||2016-08-12 08:58:01|
|Maintainer||Marshall Brown <firstname.lastname@example.org>|
Add_CostBenefit_Axis: Add cost benefit ratio axis to a decision curve plot.
cv_decision_curve: Calculate cross-validated decision curves
dcaData: Simulated dataset for package 'DecisionCurve'
dcaData_cc: Simulated dataset for package 'DecisionCurve'
decision_curve: Calculate decision curves
DecisionCurve-package: Generate and plot decision curves.
plot_clinical_impact: Plot the clinical impact curve from a DecisionCurve object.
plot_decision_curve: Plot the net benefit curves from a decision_curve object or...
plot_roc_components: Plot the components of a ROC curve by the high risk...
summary.decision_curve: Displays a useful description of a decision_curve object