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:


To install development version: