{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T09:41:43Z","timestamp":1778924503005,"version":"3.51.4"},"reference-count":55,"publisher":"SAGE Publications","issue":"10","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Stat Methods Med Res"],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:p> In this article, we present a joint modeling approach for zero-inflated longitudinal count measurements and time-to-event outcomes. For the longitudinal sub-model, a mixed effects Hurdle model is utilized, incorporating various distributional assumptions such as zero-inflated Poisson, zero-inflated negative binomial, or zero-inflated generalized Poisson. For the time-to-event sub-model, a Cox proportional hazard model is applied. For the functional form linking the longitudinal outcome history to the hazard of the event, a linear combination is used. This combination is derived from the current values of the linear predictors of Hurdle mixed effects. Some other forms are also considered, including a linear combination of the current slopes of the linear predictors of Hurdle mixed effects as well as the shared random effects. A Markov chain Monte Carlo method is implemented for Bayesian parameter estimation. Dynamic prediction using joint modeling is highly valuable in personalized medicine, as discussed here for joint modeling of zero-inflated longitudinal count measurements and time-to-event outcomes. We assess and demonstrate the effectiveness of the proposed joint models through extensive simulation studies, with a specific emphasis on parameter estimation and dynamic predictions for both over-dispersed and under-dispersed data. We finally apply the joint model to longitudinal microbiome pregnancy and HIV data sets. <\/jats:p>","DOI":"10.1177\/09622802241268466","type":"journal-article","created":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T10:33:23Z","timestamp":1728383603000},"page":"1731-1767","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Joint modeling of zero-inflated longitudinal measurements and time-to-event outcomes with applications to dynamic prediction"],"prefix":"10.1177","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8574-1750","authenticated-orcid":false,"given":"Mojtaba","family":"Ganjali","sequence":"first","affiliation":[{"name":"Department of Statistics, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2100-4532","authenticated-orcid":false,"given":"Taban","family":"Baghfalaki","sequence":"additional","affiliation":[{"name":"Inserm, Bordeaux Population Health Research Center (BPH) U1219, University of Bordeaux, ISPED, Bordeaux, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Narayanaswamy","family":"Balakrishnan","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, McMaster University, Hamilton, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2024,10,7]]},"reference":[{"key":"bibr1-09622802241268466","doi-asserted-by":"publisher","DOI":"10.1038\/nrg3182"},{"key":"bibr2-09622802241268466","doi-asserted-by":"publisher","DOI":"10.1038\/nature18850"},{"key":"bibr3-09622802241268466","doi-asserted-by":"publisher","DOI":"10.1016\/j.chom.2014.08.014"},{"key":"bibr4-09622802241268466","doi-asserted-by":"publisher","DOI":"10.1002\/sim.7063"},{"key":"bibr5-09622802241268466","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0170543"},{"key":"bibr6-09622802241268466","doi-asserted-by":"publisher","DOI":"10.1186\/s40168-017-0295-1"},{"key":"bibr7-09622802241268466","volume":"10","author":"Zhou Y","year":"2015","journal-title":"PLoS ONE"},{"key":"bibr8-09622802241268466","doi-asserted-by":"publisher","DOI":"10.3389\/fmicb.2018.01683"},{"key":"bibr9-09622802241268466","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2007.00894.x"},{"key":"bibr10-09622802241268466","doi-asserted-by":"publisher","DOI":"10.1111\/biom.13515"},{"key":"bibr11-09622802241268466","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/1.4.465"},{"key":"bibr12-09622802241268466","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxaa012"},{"key":"bibr13-09622802241268466","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.202100322"},{"key":"bibr14-09622802241268466","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1008473"},{"key":"bibr15-09622802241268466","doi-asserted-by":"publisher","DOI":"10.1002\/sim.8767"},{"key":"bibr16-09622802241268466","doi-asserted-by":"publisher","DOI":"10.1177\/0962280216659312"},{"key":"bibr17-09622802241268466","doi-asserted-by":"publisher","DOI":"10.1177\/09622802211002868"},{"key":"bibr18-09622802241268466","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2010.01546.x"},{"key":"bibr19-09622802241268466","doi-asserted-by":"publisher","DOI":"10.1201\/b12208"},{"key":"bibr20-09622802241268466","unstructured":"Plummer M et al. 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