{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T15:54:19Z","timestamp":1791388459127,"version":"4.3.3"},"reference-count":29,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Large-scale population-level datasets, such as the UK Biobank and the All of Us Research Program, often lack covariates needed for a specific analysis, such as genetic or lifestyle measures, while related studies measure them. This creates a cross-population missing-data problem in which covariates are completely unobserved in the target population, rather than partially missing within one dataset. We propose an augmented transfer regression learning method for this setting. The key identifying condition is a sub-population shift assumption: the joint distribution of the outcome and observed covariates may differ across source and target populations, but the conditional distribution of the missing covariates given observed variables is invariant. We combine importance-weighted estimating equations with imputation terms for first- and second-order moments of the missing covariates. The resulting estimator is doubly robust and remains consistent if either the density ratio model or both imputation models are correctly specified. It is n1\/2-consistent and asymptotically normal and attains the semiparametric efficiency bound when both nuisance models are correctly specified.<\/jats:p>","DOI":"10.1093\/jrsssb\/qkag131","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T11:59:25Z","timestamp":1789473565000},"source":"Crossref","is-referenced-by-count":0,"title":["Augmented transfer regression learning for completely missing covariates"],"prefix":"10.1093","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2358-8113","authenticated-orcid":false,"given":"Huali","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Huazhong University of Science and Technology , Wuhan ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2826-5364","authenticated-orcid":false,"given":"Tianying","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Statistics, Colorado State University , Fort Collins, CO 80523 ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,10,7]]},"reference":[{"issue":"7","key":"2026100711244546300_qkag131-B1","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1056\/NEJMsr1809937","article-title":"The \u201call of us\u201d research program","volume":"381","author":"All of Us Research Program Investigators","year":"2019","journal-title":"The New England Journal of Medicine"},{"issue":"17","key":"2026100711244546300_qkag131-B2","doi-asserted-by":"publisher","first-page":"1298","DOI":"10.1093\/jnci\/djt185","article-title":"The healthy eating index 2005 and risk for pancreatic cancer in the NIH\u2013AARP study","volume":"105","author":"Arem","year":"2013","journal-title":"Journal of the National Cancer Institute"},{"issue":"4","key":"2026100711244546300_qkag131-B3","doi-asserted-by":"publisher","first-page":"962","DOI":"10.1111\/j.1541-0420.2005.00377.x","article-title":"Doubly robust estimation in missing data and causal inference models","volume":"61","author":"Bang","year":"2005","journal-title":"Biometrics"},{"issue":"6","key":"2026100711244546300_qkag131-B4","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1093\/ajcn\/68.6.1157","article-title":"Dietary fat intake does affect obesity!","volume":"68","author":"Bray","year":"1998","journal-title":"The American Journal of Clinical Nutrition"},{"issue":"550","key":"2026100711244546300_qkag131-B5","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1080\/01621459.2024.2393463","article-title":"Semi-supervised triply robust inductive transfer learning","volume":"120","author":"Cai","year":"2025","journal-title":"Journal of the American Statistical Association"},{"issue":"2","key":"2026100711244546300_qkag131-B6","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1214\/009053607000000947","article-title":"Semiparametric efficiency in GMM models with auxiliary data","volume":"36","author":"Chen","year":"2008","journal-title":"Annals of Statistics"},{"issue":"4","key":"2026100711244546300_qkag131-B7","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1038\/s41416-022-02053-5","article-title":"UK Biobank: A globally important resource for cancer research","volume":"128","author":"Conroy","year":"2023","journal-title":"British Journal of Cancer"},{"key":"2026100711244546300_qkag131-B8","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4612-6333-3","volume-title":"A practical guide to splines","author":"de Boor","year":"1978"},{"key":"2026100711244546300_qkag131-B9","doi-asserted-by":"crossref","DOI":"10.1201\/9781003484080","volume-title":"A first course in causal inference","author":"Ding","year":"2024"},{"key":"2026100711244546300_qkag131-B10","first-page":"3290","article-title":"A unified view of label shift estimation","volume":"33","author":"Garg","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"507","key":"2026100711244546300_qkag131-B11","doi-asserted-by":"publisher","first-page":"1159","DOI":"10.1080\/01621459.2014.880058","article-title":"Multiply robust estimation in regression analysis with missing data","volume":"109","author":"Han","year":"2014","journal-title":"Journal of the American Statistical Association"},{"issue":"161","key":"2026100711244546300_qkag131-B12","first-page":"1","article-title":"National health and nutrition examination survey: Analytic guidelines, 1999\u20132010. 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