{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T08:11:18Z","timestamp":1790151078164,"version":"4.0.1"},"reference-count":43,"publisher":"Wiley","issue":"20-22","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"vor","delay-in-days":17,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>We develop a Bayesian Additive Regression Trees with Measurement Error (BART\u2010ME) model for flexibly estimating exposure\u2010response functions when multiple covariates are measured with classical error. Unlike existing approaches, BART\u2010ME accommodates nonlinearities, interactions, correlated exposures, and correlated measurement errors, while exploiting replicate measurements to estimate the error variance\u2010covariance structure. Posterior inference is obtained via a Metropolis\u2010within\u2010Gibbs algorithm, yielding estimates of exposure\u2010response functions, variable importance, and measurement reliability. Simulation studies demonstrate that BART\u2010ME reduces bias and improves coverage compared with Na\u00efve BART analyses that ignore measurement error, even with sparse replication. In an application to first\u2010trimester ultrasound data from the NICHD Fetal Growth Studies, BART\u2010ME identified crown\u2010rump length as the most reliable predictor of gestational age at delivery and revealed nonlinear patterns attenuated by Na\u00efve methods. These results illustrate the potential of BART\u2010ME as a general framework for correcting measurement error in complex exposure settings.<\/jats:p>","DOI":"10.1002\/sim.70739","type":"journal-article","created":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T01:32:54Z","timestamp":1789781574000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bayesian Additive Regression Trees for Modeling Multiple Exposures With Measurement Error"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9863-9720","authenticated-orcid":false,"given":"Madeleine E.","family":"St. Ville","sequence":"first","affiliation":[{"name":"<i>Eunice Kennedy Shriver<\/i> National Institute of Child Health and Human Development, National Institutes of Health  Bethesda Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8698-8667","authenticated-orcid":false,"given":"Yaeji","family":"Lim","sequence":"additional","affiliation":[{"name":"Department of Applied Statistics Chung\u2010Ang University  Seoul Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9356-6545","authenticated-orcid":false,"given":"Ruijin","family":"Lu","sequence":"additional","affiliation":[{"name":"Institute for Informatics, Data Science, and Biostatistics Washington University School of Medicine  St. Louis, MO USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0276-8534","authenticated-orcid":false,"given":"Katherine L.","family":"Grantz","sequence":"additional","affiliation":[{"name":"<i>Eunice Kennedy Shriver<\/i> National Institute of Child Health and Human Development, National Institutes of Health  Bethesda Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Chen","sequence":"additional","affiliation":[{"name":"<i>Eunice Kennedy Shriver<\/i> National Institute of Child Health and Human Development, National Institutes of Health  Bethesda Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/oxfordjournals.aje.a113561"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/oxfordjournals.aje.a009202"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/sj.jea.7500332"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1289\/ehp.00108419"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.21909"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781420010138"},{"key":"e_1_2_10_8_1","first-page":"2491","article-title":"Measurement Error in Epidemiologic Studies","volume":"4","author":"Carroll R. 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