{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T09:00:39Z","timestamp":1791450039347,"version":"4.3.3"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,5,15]],"date-time":"2021-05-15T00:00:00Z","timestamp":1621036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"UK Research & Innovation, Innovation Fellowship, funded by the Medical Research Council","award":["MR\/R024847\/1"],"award-info":[{"award-number":["MR\/R024847\/1"]}]},{"name":"Australian Research Council Discovery Project","award":["DP140100441"],"award-info":[{"award-number":["DP140100441"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,12,12]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Collecting information on multiple longitudinal outcomes is increasingly common in many clinical settings. In many cases, it is desirable to model these outcomes jointly. However, in large data sets, with many outcomes, computational burden often prevents the simultaneous modeling of multiple outcomes within a single model. We develop a mean field variational Bayes algorithm, to jointly model multiple Gaussian, Poisson, or binary longitudinal markers within a multivariate generalized linear mixed model. Through simulation studies and clinical applications (in the fields of sight threatening diabetic retinopathy and primary biliary cirrhosis), we demonstrate substantial computational savings of our approximate approach when compared to a standard Markov Chain Monte Carlo, while maintaining good levels of accuracy of model parameters.<\/jats:p>","DOI":"10.1093\/biostatistics\/kxab021","type":"journal-article","created":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T19:39:51Z","timestamp":1619638791000},"page":"177-192","source":"Crossref","is-referenced-by-count":6,"title":["Fast approximate inference for multivariate longitudinal data"],"prefix":"10.1093","volume":"24","author":[{"given":"David M","family":"Hughes","sequence":"first","affiliation":[{"name":"Department of Health Data Science, Waterhouse Building , Block F, University of Liverpool, 1-5 Brownlow Street, Liverpool, L69 3GL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marta","family":"Garc\u00eda-Fi\u00f1ana","sequence":"additional","affiliation":[{"name":"Department of Health Data Science, Waterhouse Building , Block F, University of Liverpool, 1-5 Brownlow Street, Liverpool, L69 3GL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matt P","family":"Wand","sequence":"additional","affiliation":[{"name":"School of Mathematical and Physical Sciences, University of Technology Sydney , P.O. 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