Sequential Monte Carlo methods in Bayesian joint models for longitudinal and time-to-event data

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dc.contributor.authorAlvares, Danilo
dc.contributor.authorArmero, Carmen
dc.contributor.authorForte, Anabel
dc.contributor.authorChopin, Nicolas
dc.date.accessioned2025-01-20T23:56:21Z
dc.date.available2025-01-20T23:56:21Z
dc.date.issued2021
dc.description.abstractThe statistical analysis of the information generated by medical follow-up is a very important challenge in the field of personalized medicine. As the evolutionary course of a patient's disease progresses, his/her medical follow-up generates more and more information that should be processed immediately in order to review and update his/her prognosis and treatment. Hence, we focus on this update process through sequential inference methods for joint models of longitudinal and time-to-event data from a Bayesian perspective. More specifically, we propose the use of sequential Monte Carlo (SMC) methods for static parameter joint models with the intention of reducing computational time in each update of the full Bayesian inferential process. Our proposal is very general and can be easily applied to most popular joint models approaches. We illustrate the use of the presented sequential methodology in a joint model with competing risk events for a real scenario involving patients on mechanical ventilation in intensive care units (ICUs).
dc.fuente.origenWOS
dc.identifier.doi10.1177/1471082X20916088
dc.identifier.eissn1477-0342
dc.identifier.issn1471-082X
dc.identifier.urihttps://doi.org/10.1177/1471082X20916088
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/95148
dc.identifier.wosidWOS:000535029200001
dc.issue.numero1-2
dc.language.isoen
dc.pagina.final181
dc.pagina.inicio161
dc.revistaStatistical modelling
dc.rightsacceso restringido
dc.subjectBayesian analysis
dc.subjectIBIS algorithm
dc.subjectJoint models
dc.subjectsequential inference
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleSequential Monte Carlo methods in Bayesian joint models for longitudinal and time-to-event data
dc.typeartículo
dc.volumen21
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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