{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T07:17:15Z","timestamp":1788160635156,"version":"build-2803163510"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In this paper, we propose a Bayesian multivariate network meta-regression model to compare multiple treatments used to treat cardiovascular and diabetes diseases, where the multivariate aggregate outcomes include low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides. We assume a log-linear regression model for the standard deviation of the treatment random effects to overcome the difficulty that some treatments may present only in a single study. As the within-study sample covariance matrix $\\boldsymbol {S}$ is partially observed or completely missing and the within-study sample correlations are not observed at all, we postulate a hierarchical structure on the unknown within-study covariance matrices. We further develop a Markov chain Monte Carlo sampling algorithm to sample from the posterior distribution and a Monte Carlo procedure to rank the treatment effects for the multivariate outcomes. DIC is used for model comparison. Two variations of DIC are further developed to quantify (i) the overall improvement in the fit and (ii) the gain in the fit of each outcome due to the multivariate model versus the univariate model alone. A detailed analysis of the aggregate data from real randomized controlled trials is carried out to further demonstrate the proposed methodology.<\/jats:p>","DOI":"10.1093\/biomtc\/ujag144","type":"journal-article","created":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T12:06:44Z","timestamp":1785586004000},"source":"Crossref","is-referenced-by-count":0,"title":["Bayesian network meta-regression models for multivariate aggregate responses with partially observed or completely missing within-treatment sample covariance matrices"],"prefix":"10.1093","volume":"82","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6610-3549","authenticated-orcid":false,"given":"Simiao","family":"Gao","sequence":"first","affiliation":[{"name":"Department of Statistics, University of Connecticut , Storrs, CT 06269 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7985-7204","authenticated-orcid":false,"given":"Sungduk","family":"Kim","sequence":"additional","affiliation":[{"name":"Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute , Rockville, MD 20850 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1935-2447","authenticated-orcid":false,"given":"Ming-Hui","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of Connecticut , Storrs, CT 06269 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arvind K","family":"Shah","sequence":"additional","affiliation":[{"name":"Biostatistics and Research Decision Sciences, Merck & Co., Inc. , Rahway, NJ 07065 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianxin","family":"Lin","sequence":"additional","affiliation":[{"name":"Biostatistics and Research Decision Sciences, Merck & Co., Inc. , Rahway, NJ 07065 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph G","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of North Carolina at Chapel Hill , Chapel Hill, NC 27599 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"2026083102563203300_bib1","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1177\/0272989X05282643","article-title":"The interpretation of random-effects meta-analysis in decision models","volume":"25","author":"Ades","year":"2005","journal-title":"Medical Decision Making"},{"key":"2026083102563203300_bib2","doi-asserted-by":"crossref","first-page":"488","DOI":"10.15585\/mmwr.mm7218a3","article-title":"Provisional mortality data\u2014United States, 2022","volume":"72","author":"Ahmad","year":"2023","journal-title":"Morbidity and Mortality Weekly Report"},{"key":"2026083102563203300_bib3","doi-asserted-by":"crossref","first-page":"1670","DOI":"10.1016\/S0140-6736(10)61350-5","article-title":"Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000 participants in 26 randomised trials","volume":"376","author":"Baigent","year":"2010","journal-title":"Lancet"},{"key":"2026083102563203300_bib4","doi-asserted-by":"crossref","first-page":"1426","DOI":"10.1111\/j.1742-1241.2009.02168.x","article-title":"Understanding heterogeneity in meta-analysis: The role of meta-regression","volume":"63","author":"Baker","year":"2009","journal-title":"International Journal of Clinical Practice"},{"key":"2026083102563203300_bib5","volume-title":"Monte Carlo Methods in Bayesian Computation. 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