{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T06:31:22Z","timestamp":1791441082237,"version":"4.3.3"},"reference-count":15,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2016,1,11]],"date-time":"2016-01-11T00:00:00Z","timestamp":1452470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,9,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>Predicting binary events such as newborns with large birthweight is important for obstetricians in their attempt to reduce both maternal and fetal morbidity and mortality. Such predictions have been a challenge in obstetric practice, where longitudinal ultrasound measurements taken at multiple gestational times during pregnancy may be useful for predicting various poor pregnancy outcomes. The focus of this article is on developing a flexible class of joint models for the multivariate longitudinal ultrasound measurements that can be used for predicting a binary event at birth. A skewed multivariate random effects model is proposed for the ultrasound measurements, and the skewed generalized t-link is assumed for the link function relating the binary event and the underlying longitudinal processes. We consider a shared random effect to link the two processes together. Markov chain Monte Carlo sampling is used to carry out Bayesian posterior computation. Several variations of the proposed model are considered and compared via the deviance information criterion, the logarithm of pseudomarginal likelihood, and with a training-test set prediction paradigm. The proposed methodology is illustrated with data from the NICHD Successive Small-for-Gestational-Age Births study, a large prospective fetal growth cohort conducted in Norway and Sweden.<\/jats:p>","DOI":"10.1111\/biom.12463","type":"journal-article","created":{"date-parts":[[2016,1,12]],"date-time":"2016-01-12T00:27:16Z","timestamp":1452558436000},"page":"917-925","source":"Crossref","is-referenced-by-count":13,"title":["A Class of Joint Models for Multivariate Longitudinal Measurements and a Binary Event"],"prefix":"10.1093","volume":"72","author":[{"given":"Sungduk","family":"Kim","sequence":"first","affiliation":[{"name":"Biostatistics and Bioinformatics Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development , Rockville, Maryland , U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul S.","family":"Albert","sequence":"additional","affiliation":[{"name":"Biostatistics and Bioinformatics Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development , Rockville, Maryland , U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2016,1,11]]},"reference":[{"key":"2024010914453509500_biom12463-bib-0001","volume-title":"Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables","author":"Abranowitz","year":"1972"},{"key":"2024010914453509500_biom12463-bib-0002","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1080\/01621459.1993.10476321","article-title":"Bayesian analysis of binary and polychotomous response data","volume":"88","author":"Albert","year":"1993","journal-title":"Journal of the American Statistical Association"},{"key":"2024010914453509500_biom12463-bib-0003","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1002\/sim.4405","article-title":"A linear mixed model for 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