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Joint modeling of mixed skewed longitudinal responses using convolution of normal and log-normal distributions: a Bayesian approach

dc.rights.licenseopenen_US
dc.contributor.authorMALEKPOUR, R.
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorBAGHFALAKI, Taban
dc.contributor.authorGANJALI, M.
dc.contributor.authorPOURDARVISH, A.
dc.date.accessioned2024-11-18T13:27:28Z
dc.date.available2024-11-18T13:27:28Z
dc.date.issued2024-09-16
dc.identifier.issn0361-0918en_US
dc.identifier.urioai:crossref.org:10.1080/03610918.2024.2401437
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/203337
dc.description.abstractEnThis paper investigates the joint modeling of mixed ordinal and continuous longitudinal responses using a random effects model and applying a conditional approach. For the ordinal responses, a latent variable model with a logistic distribution is employed. To address skewness in the data, the model incorporates normal and log-normal convolution (NLNC) for both the error term and the random effects in the longitudinal model. Parameter estimation is carried out within a Bayesian framework using Gibbs sampling. The performance of the proposed model is evaluated through simulation studies, comparing it to joint models of mixed ordinal and continuous longitudinal responses assuming skew-normal or normal distributions. The results indicate that joint models using skew-normal or normal distributions can lead to biased parameter estimates, whereas the NLNC joint model performs better overall. Additionally, the proposed method is applied to analyze data from the British Household Panel Survey (BHPS), using life satisfaction and annual income as the correlated ordinal and continuous longitudinal responses, respectively, with annual income showing significant skewness. The results demonstrate that the proposed model provides the best fit for capturing the substantial skewness in the data.
dc.language.isoENen_US
dc.sourcecrossref
dc.title.enJoint modeling of mixed skewed longitudinal responses using convolution of normal and log-normal distributions: a Bayesian approach
dc.typeArticle de revueen_US
dc.identifier.doi10.1080/03610918.2024.2401437en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
bordeaux.journalCommunications in Statistics - Simulation and Computationen_US
bordeaux.page1-23en_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.sourcedissemin
hal.identifierhal-04788733
hal.version1
hal.date.transferred2024-11-18T13:27:31Z
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exporttrue
workflow.import.sourcedissemin
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Communications%20in%20Statistics%20-%20Simulation%20and%20Computation&rft.date=2024-09-16&rft.spage=1-23&rft.epage=1-23&rft.eissn=0361-0918&rft.issn=0361-0918&rft.au=MALEKPOUR,%20R.&BAGHFALAKI,%20Taban&GANJALI,%20M.&POURDARVISH,%20A.&rft.genre=article


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