The index of prediction accuracy: an intuitive measure useful for evaluating risk prediction models
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BACKGROUND: Many measures of prediction accuracy have been developed. However, the most popular ones in typical medical outcome prediction settings require additional investigation of calibration. METHODS: We show how rescaling the Brier score produces a measure that combines discrimination and calibration in one value and improves interpretability by adjusting for a benchmark model. We have called this measure the index of prediction accuracy (IPA). The IPA permits a common interpretation across binary, time to event, and competing risk outcomes. We illustrate this measure using example datasets. RESULTS: The IPA is simple to compute, and example code is provided. The values of the IPA appear very interpretable. CONCLUSIONS: IPA should be a prominent measure reported in studies of medical prediction model performance. However, IPA is only a measure of average performance and, by default, does not measure the utility of a medical decision.
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Calibration intercept and calibration slope are hard to interpret and provide a partial assessment of calibration; during internal validation, calibration slope can be used to gauge overfitting.
“These measures are hard to interpret and provide a partial assessment of calibration; during internal validation, calibration slope can be used to gauge overfitting.”
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- Authors
- Kattan, M. W., & Gerds, T. A.
- Affiliations
- Cleveland Clinic (US)
- University of Copenhagen (DK)
- Corresponding author
- Kattan, Michael W.
- Venue
- Diagnostic and Prognostic Research
- Published
- 2018-05-04
- Volume
- 2
- Issue
- 1
- Pages
- 7-7
- ISSN
- 2397-7523 (ISSN-L)
- PMC
- PMC6460739
- OpenAlex
- W2805456907
- Cited by
- 149
- References
- 19
- Citations by year
- 2026: 19 · 2025: 16 · 2024: 18 · 2023: 22 · 2022: 33 · 2021: 17 · 2020: 18 · 2019: 5 · 2012: 1
- FWCI
- 8.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Diagnostic and Prognostic Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- Brier score, calibration, discrimination (statistics), risk prediction model, binary outcomes, time-to-event outcomes, competing risks, model evaluation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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