pmcalibration: 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>.

Package details

AuthorStephen Rhodes [aut, cre, cph]
MaintainerStephen Rhodes <steverho89@gmail.com>
LicenseGPL-3
Version0.1.0
URL https://github.com/stephenrho/pmcalibration
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("pmcalibration")

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pmcalibration documentation built on Sept. 8, 2023, 5:10 p.m.