Calibration of Clinical Prediction Models: A Practical Guide to Interpretation, Reporting, and Recalibration

Clinical prediction models are often evaluated primarily by discrimination, although many clinical applications depend on the accuracy of absolute risk estimates.Calibration describes agreement between predicted probabilities and observed event frequencies and must be assessed separately from discrimination.This narrative methodological review focuses on models that predict binary outcomes and examines how calibration should be assessed, interpreted, reported, and updated.The calibration hierarchy and the use of flexible calibration curves are described together with calibration-in-the-large, calibration slope, the integrated calibration index, E-statistics, and the Brier score.The review explains why grouped goodness-of-fit procedures, including the Hosmer-Lemeshow test, cannot confirm adequate calibration and why performance estimates require measures of uncertainty and a validation sample sized for the desired precision.Calibration of regression and machine learning models is considered without assuming that any model class is inherently well calibrated.Methods for intercept-only updating, logistic recalibration, and non-linear post-hoc calibration are presented, with attention to evaluating updated models in data not used for the updating step.Worked examples show how the direction, magnitude, and location of miscalibration affect interpretation.Because calibration alone does not establish clinical usefulness, models intended to guide treatment should also be evaluated for clinical utility at relevant decision thresholds.A reporting checklist specifies the information needed to judge whether risk estimates are suitable for the intended population and clinical context.