{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:49:12Z","timestamp":1792792152568,"version":"4.5.0"},"reference-count":40,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T00:00:00Z","timestamp":1738281600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100020950","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["112-2118-M-005-004-MY3"],"award-info":[{"award-number":["112-2118-M-005-004-MY3"]}],"id":[{"id":"10.13039\/501100020950","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100020950","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["113-2628-M-006-003-MY3"],"award-info":[{"award-number":["113-2628-M-006-003-MY3"]}],"id":[{"id":"10.13039\/501100020950","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":[[2025,3]]},"abstract":"<jats:p>\n            \n            \n            The article proposes a robust approach to jointly modeling multiple repeated clinical measures with intricate features. More specifically, we aim to expand the scope of the multivariate linear mixed model by using the multivariate contaminated normal distribution. The proposed model, called the\u00a0multivariate contaminated normal linear mixed model with censored and missing responses (MCNLMM-CM), is designed to handle minor outliers effectively, while simultaneously accommodating censored measurements and intermittent missing responses. An expectation conditional maximization either algorithm is developed to estimate the parameters of the proposed model in situations involving missing at random responses. We also provide techniques for approximating the asymptotic standard errors of the parameters, recovering censored data, imputing missing values, and identifying outliers. A simulation study is conducted to evaluate the finite-sample properties of the parameter estimators and demonstrate the superior performance of the proposed model compared to existing models. The proposed methodology is inspired by and applied to data from the Alzheimer\u2019s disease neuroimaging initiative cohort study, which involves longitudinal clinical measurements of patients with mild cognitive impairment.\n          <\/jats:p>","DOI":"10.1177\/09622802241309349","type":"journal-article","created":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T08:12:46Z","timestamp":1738311166000},"page":"490-507","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":9,"title":["Multivariate contaminated normal linear mixed models applied to Alzheimer\u2019s disease study with censored and missing data"],"prefix":"10.1177","volume":"34","author":[{"given":"Tsung-I","family":"Lin","sequence":"first","affiliation":[{"name":"Institute of Statistics, National Chung Hsing University, Taichung,\u00a0Taiwan"},{"name":"Department of Public Health, China Medical University, Taichung,\u00a0Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0344-7954","authenticated-orcid":false,"given":"Wan-Lun","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Statistics and Institute of Data Science, National Cheng Kung University, Tainan,\u00a0Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,1,31]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.1980.10486163"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.0006-341X.1999.00625.x"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2006.09.036"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2011.01586.x"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2012.06.021"},{"key":"e_1_3_3_7_2","first-page":"1323","article-title":"Likelihood-based inference for mixed effects models with censored response using the multivariate-t distribution","volume":"23","author":"Matos LA","year":"2013","unstructured":"Matos LA, Prates MO, Chen MH, et\u00a0al. Likelihood-based inference for mixed effects models with censored response using the multivariate-t distribution. Stat Sinica 2013; 23: 1323\u20131345.","journal-title":"Stat Sinica"},{"key":"e_1_3_3_8_2","first-page":"666","article-title":"Multivariate-t nonlinear mixed models with application to censored multioutcome AIDS studies","volume":"18","author":"Lin TI","year":"2017","unstructured":"Lin TI, Wang WL. Multivariate-t nonlinear mixed models with application to censored multioutcome AIDS studies. Biostatistics 2017; 18: 666\u2013681.","journal-title":"Biostatistics"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1177\/0962280215620229"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxh001"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11749-009-0138-x"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1997.10474030"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1002\/sim.2361"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.201200001"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1002\/sim.6144"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/63.3.581"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1198\/016214502753479211"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13253-015-0242-1"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1002\/sim.7692"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1002\/sim.8554"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.202100233"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1177\/09622802221146312"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","DOI":"10.1177\/0962280219857103"},{"key":"e_1_3_3_24_2","unstructured":"Tukey JW. A survey of sampling from contaminated distributions. In: Olkin I (ed) Contributions to probability and statistics: essays in honor of Harold hotelling. Stanford Studies in Mathematics and Statistics Chapter 39 CA: Stanford University Press 1960 pp.448\u2013485."},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/81.4.633"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1002\/sim.7381"},{"key":"e_1_3_3_27_2","doi-asserted-by":"publisher","DOI":"10.3233\/JAD-161201"},{"key":"e_1_3_3_28_2","doi-asserted-by":"publisher","DOI":"10.1111\/biom.13219"},{"key":"e_1_3_3_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2022.107438"},{"key":"e_1_3_3_30_2","doi-asserted-by":"publisher","DOI":"10.2307\/2529876"},{"key":"e_1_3_3_31_2","unstructured":"Pinheiro J Bates D DebRoy S et\u00a0al. nlme: linear and nonlinear mixed effects models. R package version 3.1-131.1. 2018."},{"key":"e_1_3_3_32_2","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.201500144"},{"key":"e_1_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1977.tb01600.x"},{"key":"e_1_3_3_34_2","unstructured":"R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing Vienna Austria 2021."},{"key":"e_1_3_3_35_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1989.tb01754.x"},{"key":"e_1_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-4541-9"},{"key":"e_1_3_3_37_2","unstructured":"Akaike H. Information theory and an extension of the maximum likelihood principle. In: Petrov BN and Csaki F (eds) 2nd International symposium on information theory. 1973 pp.267\u2013281."},{"key":"e_1_3_3_38_2","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176344136"},{"key":"e_1_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.1002\/sim.1885"},{"key":"e_1_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1995.10476485"},{"key":"e_1_3_3_41_2","doi-asserted-by":"publisher","DOI":"10.2307\/2533118"}],"container-title":["Statistical Methods in Medical Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/09622802241309349","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/09622802241309349","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/09622802241309349","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T10:32:17Z","timestamp":1747737137000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/09622802241309349"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,31]]},"references-count":40,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["10.1177\/09622802241309349"],"URL":"https:\/\/doi.org\/10.1177\/09622802241309349","relation":{},"ISSN":["0962-2802","1477-0334"],"issn-type":[{"value":"0962-2802","type":"print"},{"value":"1477-0334","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,31]]}}}