A calibration hierarchy for risk models was defined: from utopia to empirical data.

By B. Van calster, D. Nieboer, Y. Vergouwe, B. De Cock, Michael J. Pencina, Ewout W. Steyerberg. Journal of Clinical Epidemiology, 2016

729 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1a64fa5aa5975ee4dfed8070d1ac391b25e8a1e2

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Citations in the corpus

Calibration: the Achilles heel of predictive analytics (2019, methodology): “The accuracy of risk estimates, relating to the agreement between the estimated and observed number of events, is called ‘ calibration ’ [4].”
A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. (2019, methodology): “, accuracy, area under the receiver operating characteristic [ROC] curve [AUC]), while the reliability of risk predictions (calibration) is often not assessed [18].”
External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges (2016, methodology): “…example, by plotting observed versus predicted risks across tenths of predicted risk, 10 using a flexible calibration plot with a smoothed non-linear curve generated using a loess smoother or splines, 10 37 or displaying observed and predicted survival curves over time for different risk groups.”
Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators (2018, methodology): “Importantly, when models are calibrated, they cannot be harmful [4,5].”
Regression diagnostics meets forecast evaluation: conditional calibration, reliability diagrams, and coefficient of determination (2021, methodology): “We have developed hierarchies of calibration in the spirit of Van Calster et al. (2016), as highlighted in Figure 1, and proposed a generic notion of conditional calibration in terms of statistical functionals.”
A tutorial on calibration measurements and calibration models for clinical prediction models (2020, background): “In a relatively recent article from the biomedical informatics literature, Walsh et al 31,32 re-emphasize the calls from Van Calster et al 33 and Riley et al 34 on the importance of calibration in addition to discrimination when evaluating predictive models.”