print.dca

internal

Diagnostic and prognostic models are typically evaluated with
measures of accuracy that do not address clinical consequences.
Decision-analytic techniques allow assessment of clinical outcomes,
but often require collection of additional information may be
cumbersome to apply to models that yield a continuous result. Decision
curve analysis is a method for evaluating and comparing prediction
models that incorporates clinical consequences, requires only the data
set on which the models are tested, and can be applied to models that
have either continuous or dichotomous results. See the following references
for details on the methods: Vickers (2006) <doi:10.1177/0272989X06295361>,
Vickers (2008) <doi:10.1186/1472-6947-8-53>,
and Pfeiffer (2020) <doi:10.1002/bimj.201800240>.

Daniel D. Sjoberg

dcurves

Decision Curve Analysis for Model Evaluation

Emily Vertosick

print.dca function

<dl><dt>x</dt>
<dd>dca object</dd>
<dt>...</dt>
<dd>not used</dd></dl>

Arguments

Print dca() object — print.dca

<dl>

<dt>x</dt>
<dd>dca object</dd>


<dt>...</dt>
<dd>not used</dd>

</dl>

print.dca: Print `dca()` object

Description

Usage

Value

Arguments

Examples