pmcalibration-package: pmcalibration: Calibration Curves for Clinical Prediction...

In pmcalibration: Calibration Curves for Clinical Prediction Models

pmcalibration: Calibration Curves for Clinical Prediction Models

Description

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) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.jclinepi.2015.12.005")}; Austin and Steyerberg (2019) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.8281")}; Austin et al. (2020) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.8570")}.

Author(s)

Maintainer: Stephen Rhodes [e-mail] [copyright holder]

See Also

Useful links:

• https://github.com/stephenrho/pmcalibration

• Report bugs at https://github.com/stephenrho/pmcalibration/issues

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