{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T12:01:25Z","timestamp":1786363285819,"version":"build-2736575974"},"reference-count":38,"publisher":"Wiley","issue":"17","license":[{"start":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T00:00:00Z","timestamp":1683763200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 HL141286"],"award-info":[{"award-number":["R01 HL141286"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2023,7,30]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Joint modeling has been a useful strategy for incorporating latent associations between different types of outcomes simultaneously, often focusing on a longitudinal continuous outcome characterized by an LME submodel and a terminal event subject to a Cox proportional hazard or parametric survival submodel. Applications to hierarchical longitudinal studies have been less frequent, particularly with respect to a binary process, which is commonly specified by a GLMM. Furthermore, many of the joint model developments have not allowed for investigations of nested effects, such as those arising from multicenter studies. To fill this gap, we propose a multilevel joint model that encompasses the LME submodel and GLMM through a Bayesian approach. Motivated by the need for timely detection of pulmonary exacerbation and characterization of irregularly observed lung function measurements in people living with cystic fibrosis (CF) receiving care across multiple centers, we apply the model to the data arising from US CF Foundation Patient Registry. In parallel, we examine the extent of bias induced by a non\u2010hierarchical model. Our simulation study and application results show that incorporating the center effect along with individual stochastic variation over time within the LME submodel improves model estimation and prediction. Given that the center effect is evident in lung function observed in the CF population, accounting for center\u2010specific power parameters by incorporating the <jats:italic>symmetric power exponential power (spep)<\/jats:italic> link function in the GLMM can facilitate more accurate conclusions in clinical studies.<\/jats:p>","DOI":"10.1002\/sim.9758","type":"journal-article","created":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T03:15:55Z","timestamp":1683861355000},"page":"2914-2927","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multilevel joint model of longitudinal continuous and\u00a0binary outcomes for hierarchically structured data"],"prefix":"10.1002","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4084-9100","authenticated-orcid":false,"given":"Grace Chen","family":"Zhou","sequence":"first","affiliation":[{"name":"Division of Biostatistics &amp; Epidemiology Cincinnati Children's Hospital Medical Center  Cincinnati Ohio USA"},{"name":"Division of Statistics and Data Science, Department of Mathematical Sciences University of Cincinnati  Cincinnati Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seongho","family":"Song","sequence":"additional","affiliation":[{"name":"Division of Statistics and Data Science, Department of Mathematical Sciences University of Cincinnati  Cincinnati Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0705-715X","authenticated-orcid":false,"given":"Rhonda D.","family":"Szczesniak","sequence":"additional","affiliation":[{"name":"Division of Biostatistics &amp; Epidemiology Cincinnati Children's Hospital Medical Center  Cincinnati Ohio USA"},{"name":"Department of Pediatrics University of Cincinnati  Cincinnati Ohio USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,5,11]]},"reference":[{"key":"e_1_2_11_2_1","first-page":"465","article-title":"Joint modelling of longitudinal measurements and event time data","volume":"1","author":"Henderson R","year":"2000","journal-title":"Biometrics"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1201\/b12208"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1214\/09\u2010BA419"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.0006\u2010341x.2000.00487.x"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/sta4.98"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/biom.13291"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1177\/0962280220950369"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/sta4.178"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.6228"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1177\/0962280214526192"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1177\/0962280218808821"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1164\/rccm.200909\u20101421OC"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1183\/09031936.00080312"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1164\/rccm.201612-2574OC"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/JTEHM.2018.2878534"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1214\/13\u2010AOAS663"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.2307\/2529876"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1136\/thoraxjnl-2011-200953"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1214\/06\u2010BA117A"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1201\/b18502-17"},{"key":"e_1_2_11_22_1","doi-asserted-by":"publisher","DOI":"10.1201\/b18502-5"},{"key":"e_1_2_11_23_1","doi-asserted-by":"publisher","DOI":"10.1201\/b10905-6"},{"key":"e_1_2_11_24_1","volume-title":"cmdstanr: R Interface to 'CmdStan'","author":"Gabry J","year":"2022"},{"key":"e_1_2_11_25_1","volume-title":"An Introduction to Multivariate Statistical Analysis","author":"Anderson T","year":"1984"},{"key":"e_1_2_11_26_1","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.201900044"},{"key":"e_1_2_11_27_1","first-page":"3571","article-title":"Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory","volume":"11","author":"Watanabe S","year":"2010","journal-title":"J Mach Learn Res"},{"key":"e_1_2_11_28_1","doi-asserted-by":"publisher","DOI":"10.1111\/1467\u20109868.00353"},{"key":"e_1_2_11_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11222\u2010013\u20109416\u20102"},{"key":"e_1_2_11_30_1","unstructured":"VehtariA GabryJ MagnussonM et al.Loo: efficient leave\u2010one\u2010out cross\u2010validation and WAIC for Bayesian models. 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