A Two-Stage Approach for Bayesian Joint Models of Longitudinal and Survival Data: Correcting Bias with Informative Prior

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dc.contributor.authorLeiva-Yamaguchi, Valeria
dc.contributor.authorAlvares, Danilo
dc.date.accessioned2025-01-20T23:54:39Z
dc.date.available2025-01-20T23:54:39Z
dc.date.issued2021
dc.description.abstractJoint models of longitudinal and survival outcomes have gained much popularity in recent years, both in applications and in methodological development. This type of modelling is usually characterised by two submodels, one longitudinal (e.g., mixed-effects model) and one survival (e.g., Cox model), which are connected by some common term. Naturally, sharing information makes the inferential process highly time-consuming. In particular, the Bayesian framework requires even more time for Markov chains to reach stationarity. Hence, in order to reduce the modelling complexity while maintaining the accuracy of the estimates, we propose a two-stage strategy that first fits the longitudinal submodel and then plug the shared information into the survival submodel. Unlike a standard two-stage approach, we apply a correction by incorporating an individual and multiplicative fixed-effect with informative prior into the survival submodel. Based on simulation studies and sensitivity analyses, we empirically compare our proposal with joint specification and standard two-stage approaches. The results show that our methodology is very promising, since it reduces the estimation bias compared to the other two-stage method and requires less processing time than the joint specification approach.
dc.description.funderNational Fund for Scientific and Technological Development (FONDECYT, Chile)
dc.fuente.origenWOS
dc.identifier.doi10.3390/e23010050
dc.identifier.eissn1099-4300
dc.identifier.urihttps://doi.org/10.3390/e23010050
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/95040
dc.identifier.wosidWOS:000611153000001
dc.issue.numero1
dc.language.isoen
dc.revistaEntropy
dc.rightsacceso restringido
dc.subjectBayesian inference
dc.subjectbias reduction
dc.subjectindividual fixed-effect
dc.subjectStan
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleA Two-Stage Approach for Bayesian Joint Models of Longitudinal and Survival Data: Correcting Bias with Informative Prior
dc.typeartículo
dc.volumen23
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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