Joint modeling of zero-inflated longitudinal measurements and time-to-event outcomes with applications to dynamic prediction
| dc.rights.license | open | en_US |
| dc.contributor.author | GANJALI, Mojtaba | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | BAGHFALAKI, Taban | |
| dc.contributor.author | BALAKRISHNAN, Narayanaswamy | |
| dc.date.accessioned | 2024-11-18T13:43:29Z | |
| dc.date.available | 2024-11-18T13:43:29Z | |
| dc.date.issued | 2024-10-07 | |
| dc.identifier.issn | 1477-0334 | en_US |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/203338 | |
| dc.description.abstractEn | 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. | |
| dc.language.iso | EN | en_US |
| dc.subject.en | Hurdle Model | |
| dc.subject.en | Integrated Nested Laplace Approximation | |
| dc.subject.en | Joint Modeling | |
| dc.subject.en | Latent Gaussian Model | |
| dc.subject.en | Spline Functions | |
| dc.subject.en | Zero-Inflated Model | |
| dc.title.en | Joint modeling of zero-inflated longitudinal measurements and time-to-event outcomes with applications to dynamic prediction | |
| dc.title.alternative | Stat Methods Med Res | en_US |
| dc.type | Article de revue | en_US |
| dc.identifier.doi | 10.1177/09622802241268466 | en_US |
| dc.subject.hal | Sciences du Vivant [q-bio]/Santé publique et épidémiologie | en_US |
| dc.identifier.pubmed | 39373068 | en_US |
| bordeaux.journal | Statistical Methods in Medical Research | en_US |
| bordeaux.page | 9622802241268466 | en_US |
| bordeaux.hal.laboratories | Bordeaux Population Health Research Center (BPH) - UMR 1219 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | INSERM | en_US |
| bordeaux.team | BIOSTAT_BPH | en_US |
| bordeaux.peerReviewed | oui | en_US |
| bordeaux.inpress | non | en_US |
| bordeaux.import.source | pubmed | |
| hal.identifier | hal-04788774 | |
| hal.version | 1 | |
| hal.date.transferred | 2024-11-18T13:43:32Z | |
| hal.popular | non | en_US |
| hal.audience | Internationale | en_US |
| hal.export | true | |
| workflow.import.source | pubmed | |
| dc.rights.cc | Pas de Licence CC | en_US |
| bordeaux.COinS | ctx_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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