dcaData_cc

Simulated data from a case-control study containing demographic variables,
marker values and cancer outcome. The prevalence of cancer
in the population that this data is sampled from is approximately 0.11.

datasets

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.

Marshall Brown

DecisionCurve

Calculate and Plot Decision Curves

dcaData_cc function

A data frame with 500 rows and 6 variables:<ul>
<li>Age: Age in years.</li>
<li>Female: Indicator for female gender.</li>
<li>Smokes: Indicator for smoking status.</li>
<li>Marker1: simulated biomarker.</li>
<li>Marker2: simulated biomarker.</li>
<li>Cancer: Indicator for cancer.</li>
</ul>

dcaData_cc: Simulated dataset for package 'DecisionCurve'

Description

Usage

Arguments

Format