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Small datasets reduce a model\u2019s predictive performance, generalisability and fairness, and may lead to harm. To help identify (minimum) sample sizes required for model development or updating, various sample size calculations exist but their underlying theory is based on standard (unpenalised) regression and focus on minimising overfitting. To address this, we propose a more general approach, which allows extension to any machine learning or AI-based modelling method, and to any performance metric (estimand) of interest.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The proposed approach draws\u00a0samples from anticipated posterior distributions\u00a0to\u00a0examine\u00a0the impact on degradation in a model\u2019s predictive performance compared to a reference model. It requires researchers to provide candidate predictors (features), a reference model (e.g., based on mean outcome incidence, predictor weights and c-statistics of previous models), and a (existing, synthetic or pilot) dataset reflecting the joint distribution of candidate predictors in the target population.\u00a0A\u00a0fully simulation-based approach\u00a0then\u00a0generates thousands of models conditional on a chosen sample size and model development strategy,\u00a0to\u00a0produce\u00a0posterior distributions of individual predictions and model performance (degradation) metrics, to inform required sample sizes. To improve computational speed for penalised regression, we also propose a one-sample Bayesian approximation that combines shrinkage priors with a likelihood decomposed into sample size and Fisher\u2019s unit information.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The approaches are illustrated when developing models for\u00a0pre-eclampsia\u00a0using logistic regression (unpenalised, uniform shrinkage, lasso or ridge) and random forests; and are shown to encompass existing sample size calculation criteria whilst also providing model assurance probabilities, instability metrics, and degradation statistics about calibration, discrimination, clinical utility, prediction error and fairness. Uncertainty in the reference model can also be\u00a0accounted for\u00a0in the calculations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our approach provides a general\u00a0framework for\u00a0(minimum) sample size calculations for developing or updating a prediction model, and is applicable for any statistical, machine learning or\u00a0AI-based development method for supervised learning. Crucially, the\u00a0recommended (minimum) sample size will depend\u00a0on users\u2019 chosen estimands and model development\u00a0(or\u00a0updating)\u00a0approach. Example R and Stata code is provided.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12874-026-02856-7","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T06:26:17Z","timestamp":1777443977000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A general sample size framework for developing or updating a predictive algorithm: with application to clinical prediction models"],"prefix":"10.1186","volume":"26","author":[{"given":"Richard D.","family":"Riley","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rebecca","family":"Whittle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohsen","family":"Sadatsafavi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Glen P.","family":"Martin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Pate","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gary S.","family":"Collins","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joie","family":"Ensor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,29]]},"reference":[{"issue":"4","key":"2856_CR1","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1016\/S2213-2600(20)30559-2","volume":"9","author":"RK Gupta","year":"2021","unstructured":"Gupta RK, Harrison EM, Ho A, Docherty AB, Knight SR, van Smeden M, et al. 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