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Calculating the sample size required for developing a clinical prediction model
Open accessAbstract
Clinical prediction models aim to predict outcomes in individuals, to inform diagnosis or prognosis in healthcare. Hundreds of prediction models are published in the medical literature each year, yet many are developed using a dataset that is too small for the total number of participants or outcome events. This leads to inaccurate predictions and consequently incorrect healthcare decisions for some individuals. In this article, the authors provide guidance on how to calculate the sample size required to develop a clinical prediction model.
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“In step three, the sample size needed to reduce model overfitting is calculated, assuming an expected shrinkage rate of 10%, with an expected number of variables P to be included in the final predictive model of 30: S is the expected shrinkage factor, and the noise ratio evaluation index R2cs is 0.3; the calculated sample size is 740 cases.”
From Machine learning-driven prediction model for successful weaning of patients from mechanical ventilation in ICU (2026) · cites this paper as Riley [13]
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In the citing paper
“Genomic and molecular data support integration of tumour biology and genetic risk into decision-making, particularly in precision medicine applications [42].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [42]
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“The sample size calculated this way is the minimum for a standard statistical model.”
From Developing clinical prediction models: a step-by-step guide (2024) · cites this paper as [50]
In the citing paper
“Sample size calculations50 indicated a minimum sample of 2082 patients, which is larger than the available sample, raising concerns about possible overfitting issues (step 6).”
From Developing clinical prediction models: a step-by-step guide (2024) · cites this paper as [50]
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Metadata
- Authors
- Riley, R. D., Ensor, J., Snell, K. I. E., Harrell, F. E., Martin, G. P., Reitsma, J. B., Moons, K. G. M., Collins, G., & van Smeden, M.
- Affiliations
- Keele University (GB)
- Vanderbilt University (US)
- Manchester Academic Health Science Centre (GB)
- University Medical Center Utrecht (NL)
- Corresponding author
- Riley, Richard D
- ORCID
- Venue
- BMJ
- Publisher
- BMJ
- Published
- 2020-03-18
- Volume
- 368
- Pages
- m441
- ISSN
- 1756-1833 (ISSN-L)
- OpenAlex
- W3012413426
- Cited by
- 2,917
- References
- 80
- Citations by year
- 2026: 863 · 2025: 606 · 2024: 531 · 2023: 402 · 2022: 275 · 2021: 174 · 2020: 58
- FWCI
- 160.59
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.bmj.com/company/legal-information/terms-conditions/legal-information/tdm-licencepolicy (tdm)
- Locations
- BMJ (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion, other-oa)
- Keele Research Repository (Keele University) (submittedVersion, cc-by)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- HighWire Press Open Archive (submittedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
- Funders
- Georgia Clinical and Translational Science Alliance (UL1TR002243)
- Cancer Research UK (27294)
- National Institute for Health and Care Research (SPCR-142)
- Department of Health and Social Care
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (91208004, 918.10.615, project 9120.8004)
- National Institutes of Health (UL1TR002243)
- NIHR School for Primary Care Research
- National Center for Advancing Translational Sciences (TR002243)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- sample size determination, clinical prediction model, model development, prediction accuracy, prognostic model, diagnostic model, small sample size
- MeSH terms
- Clinical Decision-Making, Forecasting, Humans, Models, Theoretical, Sample Size
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