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Description

Calibration Curves for Clinical Prediction Models.

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>.

Calibration Curves for Clinical Prediction Models

A clinical prediction model should produce calibrated risk predictions, which means the predicted probabilities should align with observed probabilities. There are various ways of assessing calibration (this paper covers calibration in more detail). pmcalibration implements calibration curves for binary and (right censored) time-to-event outcomes and calculates metrics used to assess the correspondence between predicted and observed outcome probabilities (the 'integrated calibration index' or $ICI$, aka $E_{avg}$, as well as $E_{50}$, $E_{90}$, and $E_{max}$).

A goal of pmcalibration is to implement a range of methods for estimating a smooth relationship between predicted and observed probabilities and to provide confidence intervals for calibration metrics (via bootstrapping or simulation based inference).

To install:

install.packages("pmcalibration")

To install development version:

devtools::install_github("https://github.com/stephenrho/pmcalibration")
Metadata

Version

0.1.0

License

Unknown

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