{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T16:48:30Z","timestamp":1785343710018,"version":"3.55.0"},"reference-count":44,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T00:00:00Z","timestamp":1739836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T00:00:00Z","timestamp":1739836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100023452","name":"Hanarth Fonds","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100023452","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2025,2,28]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>For clinical prediction applications, we are often faced with small sample size data compared to the number of covariates. Such data pose problems for variable selection and prediction, especially when the covariate\u2010response relationship is complicated. To address these challenges, we propose to incorporate external information on the covariates into Bayesian additive regression trees (BART), a sum\u2010of\u2010trees prediction model that utilizes priors on the tree parameters to prevent overfitting. To incorporate external information, an empirical Bayes (EB) framework is developed that estimates, assisted by a model, prior covariate weights in the BART model. The proposed EB framework enables the estimation of the other prior parameters of BART as well, rendering an appealing and computationally efficient alternative to cross\u2010validation. We show that the method finds relevant covariates and that it improves prediction compared to default BART in simulations. If the covariate\u2010response relationship is non\u2010linear, the method benefits from the flexibility of BART to outperform regression\u2010based learners. Finally, the benefit of incorporating external information is shown in an application to diffuse large B\u2010cell lymphoma prognosis based on clinical covariates, gene mutations, DNA translocations, and DNA copy number data.<\/jats:p>","DOI":"10.1002\/sim.70004","type":"journal-article","created":{"date-parts":[[2025,2,18]],"date-time":"2025-02-18T11:13:41Z","timestamp":1739877221000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Adaptive Use of Co\u2010Data Through Empirical Bayes for Bayesian Additive Regression Trees"],"prefix":"10.1002","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0134-1897","authenticated-orcid":false,"given":"Jeroen M.","family":"Goedhart","sequence":"first","affiliation":[{"name":"Department of Epidemiology &amp; Data Science, Amsterdam Public Health Research Institute Amsterdam University Medical Centers Location AMC  Noord Holland The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8771-1742","authenticated-orcid":false,"given":"Thomas","family":"Klausch","sequence":"additional","affiliation":[{"name":"Department of Epidemiology &amp; 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