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The constraints ensure that the averaged risk with the biomarker included is as close as that from the standard model. Our method allows different distributions of the standard risk predictors between the target population and the source population of the study data, and relies on the study data to inform the relationship between the biomarker and the standard predictors. We develop the large\u2010sample theory of our method for parameters and measures for the predictive accuracy, and perform simulation studies to assess the finite\u2010sample performance of our method. We apply our method to analyze a case\u2013control study of breast cancer to develop a model that includes both standard breast cancer risk predictors and the biomarker mammographic density.<\/jats:p>","DOI":"10.1002\/cjs.70058","type":"journal-article","created":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T07:56:31Z","timestamp":1785830191000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improved risk prediction via cross\u2010domain calibration in a retrospective case\u2010control study"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2875-8652","authenticated-orcid":false,"given":"Ge","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Mathematics and Statistics Portland State University  Portland Oregon USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanyuan","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Statistics Penn State University  University Park Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaqi","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Statistics, School of Science Minzu University of China  Beijing China"},{"name":"Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania  Philadelphia Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,4]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0258(19970130)16:2<133::AID-SIM476>3.0.CO;2-C"},{"key":"e_1_2_12_4_1","first-page":"1","article-title":"Generalizing from several related classification tasks to a new unlabeled sample","volume":"24","author":"Blanchard G.","year":"2011","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_12_5_1","first-page":"440","volume-title":"Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics","author":"Blitzer J.","year":"2007"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/86.8.620"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/87.9.670"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000016"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/87.21.1622"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.2820741315"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10985-024-09628-9"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/92.2.399"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2015.1123157"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214508000000120"},{"key":"e_1_2_12_16_1","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/djj332"},{"key":"e_1_2_12_17_1","unstructured":"Chen L.andChen J.(2015).Cancer Absolute Risk Projection with Incomplete Predictor Variables. 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