Evaluation of clinical prediction models (part 1): from development to external validation
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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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The dataset was split into training and testing subsets using a 70/30 ratio, with 70% for model development and 30% held out for independent performance assessment.
“To ensure validation, the dataset was split into training and testing subsets using a 70/30 ratio, with 70% of the data reserved for model development and 30% held out for independent performance assessment”
From A hybrid fuzzy logic–Random Forest model to predict psychiatric treatment order outcomes: an interpretable tool for legal decision support (2025) · cites this paper as Collins et al., 2024
The “Random split” section uses a 70/30 development–test split to illustrate performance evaluation, but it does not document how the citing authors split their dataset. It also says that calling the resulting subsets “independent” is a misnomer and generally advises against split-sample validation, so it does not support the claim that this split ensured independent validation. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe split sample approach might encourage researchers to repeat the procedure until they obtain satisfactory results.
“Further, it might encourage researchers to repeat the procedure until they obtain satisfactory results.”
From Developing clinical prediction models: a step-by-step guide (2024) · cites this paper as [88]
Implementation of k-fold cross validation and bootstrapping can be computationally demanding when multiple imputation of missing data is needed.
“However, implementation of k-fold cross validation and bootstrapping can be computationally demanding when multiple imputation of missing data is needed.”
From Developing clinical prediction models: a step-by-step guide (2024) · cites this paper as [88]
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Predicting Diagnostic Progression to Schizophrenia or Bipolar Disorder via Machine Learning
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Predicting daily recovery during long-term endurance training using machine learning analysis
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Precision psychiatry: thinking beyond simple prediction models – enhancing causal predictions
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Assessing methodological quality
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External Validation of AI Models for Skin Diseases: A Systematic Review
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Convergence of machine learning and genomics for precision oncology
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Early Treatment Response to Mepolizumab Predicts Clinical Remission in Severe Eosinophilic Asthma
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The urgency of centering safety-net organizations in AI governance
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Internal Validation of a Machine Learning-Based CDSS for Antimicrobial Stewardship
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A Risk Prediction Tool for Invasive Melanoma
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Machine learning in oncological pharmacogenomics: advancing personalized chemotherapy
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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
- Venue
- BMJ
- Publisher
- BMJ
- Published
- 2024-01-08
- Volume
- 384
- Pages
- e074819
- ISSN
- 1756-1833 (ISSN-L)
- 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
- 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
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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