Cited by 814Statistical Methods in EpidemiologyArtificial Intelligence in HealthcareMeta-analysis and systematic reviews

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Evaluation of clinical prediction models (part 1): from development to external validation

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Collins, Gary S, Dhiman, Paula, Ma, Jie, Schlussel, Michael M, Archer, Lucinda, Van Calster, Ben, Harrell, Frank E, Martin, Glen P, Moons, Karel G M, van Smeden, Maarten, Sperrin, Matthew, Bullock, Garrett S, Riley, Richard D·2024·BMJ

DOI 10.1136/bmj-2023-074819PMC PMC10772854

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Abstract

Evaluating the performance of a clinical prediction model is crucial to establish its predictive accuracy in the populations and settings intended for use. In this article, the first in a three part series, Collins and colleagues describe the importance of a meaningful evaluation using internal, internal-external, and external validation, as well as exploring heterogeneity, fairness, and generalisability in model performance.

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Fig 1 Different study design and approaches to develop and evaluate the performance of a multivariable prediction model (D=development; V=validation (evaluation)). Adapted from Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ 2015;350:g7594.17 *A study can include more than one analysis typeCC BY · PubMed Central

Fig 2 Variability and overestimation of apparent performance compared to large sample performance, for a model to predict in-hospital mortality within 28 days of trauma injury with increasing sample size of the model development study. ĉ denotes the apparent performance estimate and clarge denotes the performance of the model in the entire CRASH-2 population (n=20 207).22 Red lines=mean ĉ−clarge for each sample size. Jitter has been added to aid display. ĉ−clarge=0 implies no systematic overestimation or underestimation of ĉCC BY · PubMed Central

Fig 3 Variability and overestimation of the apparent and internal (split sample and bootstrap) validation performance compared with the large sample performance, for a model to predict in-hospital mortality within 28 days of trauma injury with increasing sample size of the model development study. ĉ denotes the apparent performance estimate and clarge denotes the performance of the model in the entire CRASH-2 population (n=20 207). The red lines denote the mean ĉ−clarge for each sample size and for each approach. Jitter has been added to aid display. Split sample (apparent, 70%)=70% of the available data were used to develop the model, and its (apparent) performance evaluated in this same data. Split sample (validation, 30%)=the performance of the model (developed in 70% of the available data) in the remaining 30% of the data. ĉ−clarge=0 implies no systematic overestimation or underestimation of ĉCC BY · PubMed Central

Fig 4 Graphical illustration of k-fold cross validation. Non-shaded parts used for model development; shaded part used for testingCC BY · PubMed Central

Fig 5 Graphical illustration of internal-external cross validation. Non-shaded parts used for model development; shaded part used for testingCC BY · PubMed Central

Fig 6 Internal-external cross validation of the ISARIC (International Severe Acute Respiratory and Emerging Infection Consortium) 4C model. Adapted from Gupta et al.2 Estimates and confidence intervals taken from original paper where they were reported to two decimal places.CC BY · PubMed Central

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Authors: Collins, G. S., Dhiman, P., Ma, J., Schlussel, M. M., Archer, L., Van Calster, B., Harrell, F. E., Martin, G. P., Moons, K. G. M., van Smeden, M., Sperrin, M., Bullock, G. S., & Riley, R. D.
Affiliations: Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
University of Oxford (GB)
National Institute for Health and Care Research (GB)
KU Leuven, Department of Development and Regeneration, Leuven, Belgium
Vanderbilt University (US)
Manchester Academic Health Science Centre (GB)
Utrecht University (NL)
Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
ORCID: Collins, Gary S
Dhiman, Paula
Ma, Jie
Archer, Lucinda
Harrell, Frank E
Martin, Glen P
Moons, Karel G M
Sperrin, Matthew
Bullock, Garrett S
Riley, Richard D
Venue: BMJ
Publisher: BMJ
Published: 2024-01-08
Volume: 384
Pages: e074819
ISSN: 1756-1833 (ISSN-L)
DOI: 10.1136/bmj-2023-074819
PMC: PMC10772854
OpenAlex: W4390674087
Cited by: 798
References: 65
Citations by year: 2026: 464 · 2025: 253 · 2024: 81
FWCI: 274.95
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: BMJ (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
University of Birmingham Research Portal (University of Birmingham) (publishedVersion, cc-by)
Research Explorer (The University of Manchester) (publishedVersion, cc-by)
Lirias (KU Leuven) (submittedVersion, cc-by)
Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
HighWire Press Open Archive (submittedVersion)
Funders: Cancer Research UK
KU Leuven
Engineering and Physical Sciences Research Council
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Keywords: clinical prediction model, model evaluation, external validation, internal validation, prediction accuracy, model heterogeneity, fairness, generalizability
MeSH terms: Humans, Prognosis, Models, Statistical

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