pmcalibration: Calibration Curves for Clinical Prediction Models (original) (raw)
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>.
Version: | 0.1.0 |
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Imports: | Hmisc, MASS, checkmate, chk, mgcv, splines, graphics, stats, methods, survival, pbapply, parallel |
Suggests: | knitr, rmarkdown, data.table, ggplot2, rms, simsurv |
Published: | 2023-09-06 |
DOI: | 10.32614/CRAN.package.pmcalibration |
Author: | Stephen Rhodes [aut, cre, cph] |
Maintainer: | Stephen Rhodes |
BugReports: | https://github.com/stephenrho/pmcalibration/issues |
License: | GPL-3 |
URL: | https://github.com/stephenrho/pmcalibration |
NeedsCompilation: | no |
Citation: | pmcalibration citation info |
Materials: | README NEWS |
CRAN checks: | pmcalibration results |
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