{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T18:16:51Z","timestamp":1770833811179,"version":"3.50.1"},"reference-count":21,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2016,2,5]],"date-time":"2016-02-05T00:00:00Z","timestamp":1454630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000071","name":"NICHD","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000071","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,1,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>Longitudinal monitoring of biomarkers is often helpful for predicting disease or a poor clinical outcome. We consider the prediction of both large and small for gestational age births by using longitudinal ultrasound measurements, and we attempt to identify subgroups of women for whom prediction is more (or less) accurate, should they exist. We propose a tree-based approach to identifying such subgroups, and a pruning algorithm which explicitly incorporates a desired type I error rate, allowing us to control the risk of false discovery of subgroups. The methods proposed are applied to data from the Scandinavian Fetal Growth Study and are evaluated via simulations.<\/jats:p>","DOI":"10.1111\/rssa.12182","type":"journal-article","created":{"date-parts":[[2016,2,6]],"date-time":"2016-02-06T04:23:04Z","timestamp":1454732584000},"page":"247-261","source":"Crossref","is-referenced-by-count":7,"title":["Identifying Subgroups of Enhanced Predictive Accuracy from Longitudinal Biomarker Data by Using Tree-Based Approaches: Applications to Fetal Growth"],"prefix":"10.1093","volume":"180","author":[{"given":"Jared C.","family":"Foster","sequence":"first","affiliation":[{"name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development , Bethesda , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danping","family":"Liu","sequence":"additional","affiliation":[{"name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development , Bethesda , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul S.","family":"Albert","sequence":"additional","affiliation":[{"name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development , Bethesda , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aiyi","family":"Liu","sequence":"additional","affiliation":[{"name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development , Bethesda , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2016,2,5]]},"reference":[{"key":"2023030310212656300_rssa12182-cit-0001","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1002\/sim.4405","article-title":"A linear mixed model for predicting a binary event from longitudinal data under random effects misspecification","volume":"31","author":"Albert","year":"2012","journal-title":"Statist. 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