cal_metrics: Calculate calibration metrics from calibration curve

In pmcalibration: Calibration Curves for Clinical Prediction Models

Calculate calibration metrics from calibration curve

Description

Calculates metrics used for summarizing calibration curves. See Austin and Steyerberg (2019)

Usage

cal_metrics(p, p_c)

Arguments

p	predicted probabilities
p_c	probabilities from the calibration curve

Value

a named vector of metrics based on absolute difference between predicted and calibration curve implied probabilities d = abs(p - p_c)

• Eavg - average absolute difference (aka integrated calibration index or ICI)

• E50 - median absolute difference

• E90 - 90th percentile absolute difference

• Emax - maximum absolute difference

• ECI - average squared difference. Estimated calibration index (Van Hoorde et al. 2015)

References

Austin PC, Steyerberg EW. (2019) The Integrated Calibration Index (ICI) and related metrics for quantifying the calibration of logistic regression models. Statistics in Medicine. 38, pp. 1–15. https://doi.org/10.1002/sim.8281

Van Hoorde, K., Van Huffel, S., Timmerman, D., Bourne, T., Van Calster, B. (2015). A spline-based tool to assess and visualize the calibration of multiclass risk predictions. Journal of Biomedical Informatics, 54, pp. 283-93

Van Calster, B., Nieboer, D., Vergouwe, Y., De Cock, B., Pencina M., Steyerberg E.W. (2016). A calibration hierarchy for risk models was defined: from utopia to empirical data. Journal of Clinical Epidemiology, 74, pp. 167-176

Examples

library(pmcalibration)

LP <- rnorm(100) # linear predictor
p_c <- invlogit(LP) # actual probabilities
p <- invlogit(LP*1.3) # predicted probabilities that are miscalibrated

cal_metrics(p = p, p_c = p_c)

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