{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:11:16Z","timestamp":1764173476583,"version":"3.46.0"},"reference-count":58,"publisher":"Wiley","issue":"18-19","license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"vor","delay-in-days":6,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81671633"],"award-info":[{"award-number":["81671633"]}],"id":[{"id":"10.13039\/501100001809","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":[[2025,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>In the realm of clinical medical research, semi\u2010competing risks data are usually observed in practice, yet there are few studies on the joint models of longitudinal and semi\u2010competing risks data. In this paper, a joint model for longitudinal and semi\u2010competing risks data is proposed. Based on the expectile regression, a linear mixed\u2010effects longitudinal sub\u2010model is formulated, and a Cox proportional hazards survival sub\u2010model is considered under the framework of semi\u2010competing risks. The two sub\u2010models are linked by a shared longitudinal trajectory function. To accommodate the time\u2010varying relationship between the longitudinal response variable and covariates, as well as to introduce flexibility to the structural linkage between longitudinal and survival processes, we incorporate the time\u2010varying coefficients into the joint model in the form of nonparametric functions. The simultaneous Bayesian inference method is utilized to estimate the model parameters, which not only overcomes the convergence problem, but also improves the accuracy of the parameter estimation while effectively reducing the computational burden. The simulation studies are conducted to assess the performance of the proposed joint model and methodology. Finally, we analyze a dataset from the Multicenter AIDS Cohort Study to illustrate the real application of the proposed model and method. In both simulation studies and empirical analyses, joint modeling methods demonstrate performance that meets expected effects.<\/jats:p>","DOI":"10.1002\/sim.70219","type":"journal-article","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T13:08:59Z","timestamp":1754572139000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bayesian Expectile Joint Model With Varying Coefficient for Longitudinal and Semi\u2010Competing Risks Data"],"prefix":"10.1002","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0013-808X","authenticated-orcid":false,"given":"Feng","family":"Gu","sequence":"first","affiliation":[{"name":"College of Mathematics and Statistics Wuhan University of Technology  Wuhan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaqing","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Mathematics and Statistics Wuhan University of 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