simb

simb.glm_cal

simb.gam_cal

simb.lowess_cal

simb.loess_cal

Simulation based inference with a calibration curve object

internal

Fit calibrations curves for clinical prediction models and calculate several associated
metrics (Eavg, E50, E90, Emax). Ideally predicted probabilities from a prediction model
should align with observed probabilities. Calibration curves relate predicted probabilities
(or a transformation thereof) to observed outcomes via a flexible non-linear smoothing function.
'pmcalibration' allows users to choose between several smoothers (regression splines, generalized
additive models/GAMs, lowess, loess). Both binary and time-to-event outcomes are supported.
See Van Calster et al. (2016) <doi:10.1016/j.jclinepi.2015.12.005>;
Austin and Steyerberg (2019) <doi:10.1002/sim.8281>;
Austin et al. (2020) <doi:10.1002/sim.8570>.

Stephen Rhodes

pmcalibration

Calibration Curves for Clinical Prediction Models

simb function

<dl><dt>cal</dt>
<dd>an object created using one of the <code>cal</code> functions</dd>
<dt>R</dt>
<dd>number of simulated replicates</dd></dl>

Arguments

Simulation based inference with a calibration curve object — simb

<dl>

<dt>cal</dt>
<dd>an object created using one of the <code>cal</code> functions</dd>


<dt>R</dt>
<dd>number of simulated replicates</dd>

</dl>

simb: Simulation based inference with a calibration curve object

Description

Usage

Value

Arguments