How to evaluate probabilistic prediction models: Key metrics

Linard Hoessly; Matthew Parry

doi:10.1016/j.jclinepi.2026.112247

How to evaluate probabilistic prediction models: Key metrics

Linard Hoessly Matthew Parry

Journal of clinical epidemiology, Vol.194, 112247

23/03/2026

: https://doi.org/10.1016/j.jclinepi.2026.112247

https://hdl.handle.net/10523/50386

Evaluation

Clinical Utility

Discrimination

Calibration

Prediction model

Probabilistic clinical prediction models play a critical role by informing healthcare professionals both in diagnosis and prognosis. To assess the qualities of a probabilistic prediction model, performance evaluation measures are used. We explain the kinds of measures that are usually considered and focus on the interpretation for some typical measures in the case of a binary outcome.

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url

https://doi.org/10.1016/j.jclinepi.2026.112247

Published (Version of record)

: 9926853270501891
: How to evaluate probabilistic prediction models: Key metrics
: Linard Hoessly
Matthew Parry
: Mathematics and Statistics; Statistics
: Journal of clinical epidemiology, Vol.194, 112247
: Elsevier
: 23/03/2026
: Copyright © The Author(s) 2026. This work was first published in Journal of Clinical Epidemiology (Elsevier). This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (https://www.creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, provided that the original work is properly attributed to the creator(s) and the source, a link to the Creative Commons license is provided, and any changes made are indicated.
: English
: Journal article