confint.fcrr

Computes confidence intervals for one or more parameters in a fitted model of class <code>fcrr</code>.

In competing risks regression, the proportional subdistribution hazards (PSH) model is popular for its direct assessment of covariate effects on the cumulative incidence function. This package allows for both penalized and unpenalized PSH regression in linear time using a novel forward-backward scan. Penalties include Ridge, Lease Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Plus (MCP), and elastic net <doi: 10.32614/RJ-2021-010>.

Eric Kawaguchi

fastcmprsk

Fine-Gray Regression via Forward-Backward Scan

confint.fcrr function

<dl><dt>object</dt>
<dd><code>fcrr</code> object (output from <code>fastCrr()</code>)</dd>
<dt>parm</dt>
<dd>a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered.</dd>
<dt>level</dt>
<dd>the confidence level required</dd>
<dt>digits</dt>
<dd>Number of significant difits to round to.</dd>
<dt>...</dt>
<dd>Additional arguments. Not implemented.</dd></dl>

Arguments

Confidence Intervals for Model Parameters — confint.fcrr

<dl>

<dt>object</dt>
<dd><code>fcrr</code> object (output from <code>fastCrr()</code>)</dd>


<dt>parm</dt>
<dd>a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered.</dd>


<dt>level</dt>
<dd>the confidence level required</dd>


<dt>digits</dt>
<dd>Number of significant difits to round to.</dd>


<dt>...</dt>
<dd>Additional arguments. Not implemented.</dd>

</dl>

confint.fcrr: Confidence Intervals for Model Parameters

Description

Usage

Value

Arguments

Details