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. Version 1.4 of the package provides an alternative framing of the decision problem for situations where treatment is the standard-of-care and a risk model might be used in order for low-risk patients (i.e., patients below some risk threshold) to opt out of treatment.
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.
Key functions are:
decision_curve
: Estimate (standardized) net benefit curves with bootstrap confidence intervals.
plot_decision_curve
: Plot a decision curve or multiple curves.
plot_clinical_impact
and plot_roc_components
: Alternative plots for the output of decision_curve
. See help files or tutorial for more info.
cv_decision_curve
: Calculate k-fold cross-validated estimates of decision curves.
The easiest way to get the package is directly from CRAN:
install.packages("DecisionCurve")
You may also download the current version of the package here:
https://github.com/mdbrown/DecisionCurve/releases
navigate to the source package and use
install.packages("../DecisionCurve_1.4.tar.gz",
repos = NULL,
type = "source")
or install the package directly from github using devtools.
## install.packages("devtools")
library(devtools)
install_github("mdbrown/DecisionCurve")
click here for a tutorial to get you started.
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.