{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T22:10:33Z","timestamp":1790719833567,"version":"4.1.0"},"reference-count":28,"publisher":"SAGE Publications","issue":"8","license":[{"start":{"date-parts":[[2018,7,2]],"date-time":"2018-07-02T00:00:00Z","timestamp":1530489600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["R01NS091307"],"award-info":[{"award-number":["R01NS091307"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Stat Methods Med Res"],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p> In the traditional joint models of a longitudinal and time-to-event outcome, a linear mixed model assuming normal random errors is used to model the longitudinal process. However, in many circumstances, the normality assumption is violated and the linear mixed model is not an appropriate sub-model in the joint models. In addition, as the linear mixed model models the conditional mean of the longitudinal outcome, it is not appropriate if clinical interest lies in making inference or prediction on median, lower, or upper ends of the longitudinal process. To this end, quantile regression provides a flexible, distribution-free way to study covariate effects at different quantiles of the longitudinal outcome and it is robust not only to deviation from normality, but also to outlying observations. In this article, we present and advocate the linear quantile mixed model for the longitudinal process in the joint models framework. Our development is motivated by a large prospective study of Huntington\u2019s disease where primary clinical interest is in utilizing longitudinal motor scores and other early covariates to predict the risk of developing Huntington\u2019s disease. We develop a Bayesian method based on the location\u2013scale representation of the asymmetric Laplace distribution, assess its performance through an extensive simulation study, and demonstrate how this linear quantile mixed model-based joint models approach can be used for making subject-specific dynamic predictions of survival probability. <\/jats:p>","DOI":"10.1177\/0962280218784757","type":"journal-article","created":{"date-parts":[[2018,7,2]],"date-time":"2018-07-02T10:17:07Z","timestamp":1530526627000},"page":"2524-2537","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":14,"title":["Bayesian quantile regression joint models: Inference and dynamic predictions"],"prefix":"10.1177","volume":"28","author":[{"given":"Ming","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4214-5809","authenticated-orcid":false,"given":"Sheng","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stacia","family":"DeSantis","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, The University of Texas Health Science Center at Houston, Houston, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2018,7,2]]},"reference":[{"key":"bibr1-0962280218784757","doi-asserted-by":"crossref","unstructured":"Self S and Pawitan Y. Modeling a marker of disease progression and onset of disease. In: AIDS epidemiology. Boston, MA: Birkh\u00e4user Boston \u2013 Springer, 1992, pp.231\u2013255.","DOI":"10.1007\/978-1-4757-1229-2_11"},{"key":"bibr2-0962280218784757","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1995.10476485"},{"key":"bibr3-0962280218784757","first-page":"809","volume":"14","author":"Tsiatis AA","year":"2004","journal-title":"Stat Sin"},{"key":"bibr4-0962280218784757","doi-asserted-by":"publisher","DOI":"10.2307\/2529876"},{"key":"bibr5-0962280218784757","doi-asserted-by":"publisher","DOI":"10.1257\/jep.15.4.143"},{"key":"bibr6-0962280218784757","doi-asserted-by":"crossref","unstructured":"Koenker R. Quantile regression. Cambridge, UK: Cambridge University Press, 2005.","DOI":"10.1017\/CBO9780511754098"},{"key":"bibr7-0962280218784757","doi-asserted-by":"publisher","DOI":"10.2307\/1913643"},{"key":"bibr8-0962280218784757","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmva.2004.05.006"},{"key":"bibr9-0962280218784757","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxj039"},{"key":"bibr10-0962280218784757","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-013-9381-9"},{"key":"bibr11-0962280218784757","doi-asserted-by":"publisher","DOI":"10.1080\/00949655.2010.496117"},{"key":"bibr12-0962280218784757","doi-asserted-by":"publisher","DOI":"10.1002\/sim.6393"},{"key":"bibr13-0962280218784757","doi-asserted-by":"crossref","unstructured":"Scott DW. Multivariate density estimation: theory, practice, and visualization. New York, NY: John Wiley & Sons, 2015.","DOI":"10.1002\/9781118575574"},{"key":"bibr14-0962280218784757","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2010.01546.x"},{"key":"bibr15-0962280218784757","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2012.01823.x"},{"key":"bibr16-0962280218784757","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1999.10473882"},{"key":"bibr17-0962280218784757","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-7152(01)00124-9"},{"key":"bibr18-0962280218784757","unstructured":"Kotz S, Kozubowski T and Podgorski K. The Laplace distribution and generalizations: a revisit with applications to communications, economics, engineering, and finance. Boston, MA: Birkh\u00e4user Boston \u2013 Springer Science & Business Media, 2012."},{"key":"bibr19-0962280218784757","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2014.931236"},{"key":"bibr20-0962280218784757","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.1972.10488991"},{"key":"bibr21-0962280218784757","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176344247"},{"key":"bibr22-0962280218784757","unstructured":"Plummer M. Jags: A program for analysis of Bayesian graphical models using Gibbs sampling. In: Proceedings of the 3rd international workshop on distributed statistical computing, volume 124. Vienna, 20\u201322 March 2003, p.125."},{"key":"bibr23-0962280218784757","doi-asserted-by":"publisher","DOI":"10.1111\/j.0006-341X.2000.00337.x"},{"key":"bibr24-0962280218784757","doi-asserted-by":"publisher","DOI":"10.1080\/10618600.1998.10474787"},{"key":"bibr25-0962280218784757","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(86)90837-8"},{"key":"bibr26-0962280218784757","doi-asserted-by":"publisher","DOI":"10.1136\/jnnp.2007.128728"},{"key":"bibr27-0962280218784757","doi-asserted-by":"publisher","DOI":"10.1001\/archneur.63.6.883"},{"key":"bibr28-0962280218784757","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(14)70238-8"}],"container-title":["Statistical Methods in Medical Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0962280218784757","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/0962280218784757","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/0962280218784757","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T21:10:01Z","timestamp":1740777001000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/0962280218784757"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,2]]},"references-count":28,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2019,8]]}},"alternative-id":["10.1177\/0962280218784757"],"URL":"https:\/\/doi.org\/10.1177\/0962280218784757","relation":{},"ISSN":["0962-2802","1477-0334"],"issn-type":[{"value":"0962-2802","type":"print"},{"value":"1477-0334","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,2]]}}}