{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:57:40Z","timestamp":1788829060061,"version":"build-2803163510"},"reference-count":39,"publisher":"Wiley","issue":"3-4","license":[{"start":{"date-parts":[[2025,1,26]],"date-time":"2025-01-26T00:00:00Z","timestamp":1737849600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2025,2,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Risk prediction models are increasingly used in healthcare to aid in clinical decision\u2010making. In most clinical contexts, model calibration (i.e., assessing the reliability of risk estimates) is critical. Data available for model development are often not perfectly balanced with the modeled outcome (i.e., individuals with vs. without the event of interest are not equally prevalent\u00a0in the data). It is common for researchers to correct for class imbalance, yet, the effect of such imbalance corrections on the calibration of machine learning models is largely unknown.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We studied the effect of imbalance corrections on model calibration for a variety of machine learning algorithms. Using extensive Monte Carlo simulations we compared the out\u2010of\u2010sample predictive performance of models developed with an imbalance correction to those developed without a correction for class imbalance across different data\u2010generating scenarios (varying sample size, the number of predictors, and event fraction). Our findings were illustrated in a case study using MIMIC\u2010III data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In all simulation scenarios, prediction models developed without a correction for class imbalance consistently had equal or better calibration performance than prediction models developed with a correction for class imbalance. The miscalibration introduced by correcting for class imbalance was characterized by an over\u2010estimation of risk and was not always able to be corrected with re\u2010calibration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Correcting for class imbalance is not always necessary and may even be harmful to clinical prediction models which aim to produce reliable risk estimates on an individual basis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/sim.10320","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T00:38:00Z","timestamp":1737938280000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["The Harms of Class Imbalance Corrections for Machine Learning Based Prediction Models: A Simulation Study"],"prefix":"10.1002","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4499-8043","authenticated-orcid":false,"given":"Alex","family":"Carriero","sequence":"first","affiliation":[{"name":"Julius Center for Health Sciences and Primary Care University Medical Center Utrecht  Utrecht The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kim","family":"Luijken","sequence":"additional","affiliation":[{"name":"Julius Center for Health Sciences and Primary Care University Medical Center Utrecht  Utrecht The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne","family":"de Hond","sequence":"additional","affiliation":[{"name":"Julius Center for Health Sciences and Primary Care University Medical Center Utrecht  Utrecht The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karel G. M.","family":"Moons","sequence":"additional","affiliation":[{"name":"Julius Center for Health Sciences and Primary Care University Medical Center Utrecht  Utrecht The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1613-7450","authenticated-orcid":false,"given":"Ben","family":"van Calster","sequence":"additional","affiliation":[{"name":"KU Leuven  Leuven Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5529-1541","authenticated-orcid":false,"given":"Maarten","family":"van Smeden","sequence":"additional","affiliation":[{"name":"Julius Center for Health Sciences and Primary Care University Medical Center Utrecht  Utrecht The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,1,26]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-77244-8_2"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.21037\/atm.2019.11.121"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-019-1466-7"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2021.01.009"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.b606"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-021-01302-4"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.3844\/jcssp.2018.777.792"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2011.12.043"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.12.035"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocac093"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2024.104666"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj\u20102022\u2010072883"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1177\/0272989X14547233"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471\u20102288\u20108\u201048"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2022.11.015"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.8086"},{"key":"e_1_2_9_18_1","unstructured":"J.Ensor E. 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