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For example, body mass index (BMI) is a derived variable constructed from two source variables: weight and height. When using a derived variable as the outcome in a statistical model, complications arise when some of the source variables have missing values. In this paper, we propose how one can define a single fully integrated Bayesian model to simultaneously impute missing values and sample from the posterior. We compare our proposed method with alternative approaches that rely on multiple imputation (MI), with examples including an analysis to estimate the risk of microcephaly (a derived variable based on sex, gestational age, and head circumference at birth) in newborns exposed to the ZIKA virus.<\/jats:p>","DOI":"10.1002\/sim.70383","type":"journal-article","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T16:24:11Z","timestamp":1769099051000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Fully\u2010Integrated Bayesian Approach for the Imputation and Analysis of Derived Outcome Variables With Missingness"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0959-1594","authenticated-orcid":false,"given":"Harlan","family":"Campbell","sequence":"first","affiliation":[{"name":"Department of Statistics University of British Columbia  Vancouver Canada"},{"name":"Health Economics and Outcomes Research Precision AQ  Vancouver Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5850-3610","authenticated-orcid":false,"given":"Tim P.","family":"Morris","sequence":"additional","affiliation":[{"name":"MRC Clinical Trials Unit at UCL University College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2375-5006","authenticated-orcid":false,"given":"Paul","family":"Gustafson","sequence":"additional","affiliation":[{"name":"Department of Statistics University of British Columbia  Vancouver Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,1,22]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamapediatrics.2025.1612"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1136\/jech.2006.051680"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.spinee.2025.03.027"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.9203"},{"key":"e_1_2_11_6_1","first-page":"1","article-title":"Adverse Fetal and Perinatal Outcomes Associated With Zika Virus Infection During Pregnancy: An Individual Participant Data Meta\u2010Analysis","volume":"83","author":"The Zika Virus Individual Participant Data Consortium","year":"2025","journal-title":"EClinicalMedicine"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2018-026092"},{"key":"e_1_2_11_8_1","unstructured":"A.Mason N.Best I.Plewis andS.Richardson \u201cInsights Into the Use of Bayesian Models for Informative Missing Data \u201d2010."},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214507000000932"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/63.3.581"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1201\/9780429492259"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9531.2009.01215.x"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.4067"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2288-12-46"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1177\/0962280214521348"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1177\/26320843231224809"},{"key":"e_1_2_11_17_1","unstructured":"J.Bartlett R.Keogh E. 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