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Joint modeling of zero-inflated longitudinal measurements and time-to-event outcomes with applications to dynamic prediction

dc.rights.licenseopenen_US
dc.contributor.authorGANJALI, Mojtaba
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorBAGHFALAKI, Taban
dc.contributor.authorBALAKRISHNAN, Narayanaswamy
dc.date.accessioned2024-11-18T13:43:29Z
dc.date.available2024-11-18T13:43:29Z
dc.date.issued2024-10-07
dc.identifier.issn1477-0334en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/203338
dc.description.abstractEnIn 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.
dc.language.isoENen_US
dc.subject.enHurdle Model
dc.subject.enIntegrated Nested Laplace Approximation
dc.subject.enJoint Modeling
dc.subject.enLatent Gaussian Model
dc.subject.enSpline Functions
dc.subject.enZero-Inflated Model
dc.title.enJoint modeling of zero-inflated longitudinal measurements and time-to-event outcomes with applications to dynamic prediction
dc.title.alternativeStat Methods Med Resen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1177/09622802241268466en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed39373068en_US
bordeaux.journalStatistical Methods in Medical Researchen_US
bordeaux.page9622802241268466en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamBIOSTAT_BPHen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.import.sourcepubmed
hal.identifierhal-04788774
hal.version1
hal.date.transferred2024-11-18T13:43:32Z
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exporttrue
workflow.import.sourcepubmed
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Statistical%20Methods%20in%20Medical%20Research&rft.date=2024-10-07&rft.spage=9622802241268466&rft.epage=9622802241268466&rft.eissn=1477-0334&rft.issn=1477-0334&rft.au=GANJALI,%20Mojtaba&BAGHFALAKI,%20Taban&BALAKRISHNAN,%20Narayanaswamy&rft.genre=article


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