Bayesian multivariate nonlinear mixed models for censored longitudinal trajectories with non-monotone missing values

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dc.article.number10.1007
dc.catalogadorgrr
dc.contributor.authorWang, Wan-Lun
dc.contributor.authorCastro Cepero, Luis Mauricio
dc.contributor.authorLin, Tsung-I
dc.date.accessioned2023-11-16T20:27:06Z
dc.date.available2023-11-16T20:27:06Z
dc.date.issued2023
dc.description.abstractThe analysis of multivariate longitudinal data may often encounter a difficult task, particularly in the presence of censored measurements induced by detection limits and intermittently missing values arising when subjects do not respond to a part of outcomes during scheduled visits. The multivariate nonlinear mixed model (MNLMM) has emerged as a promising analytical tool for multi-outcome longitudinal data following arbitrarily nonlinear profiles with random phenomena. This article presents a generalization of the MNLMM, called MNLMM-CM, designed to simultaneously accommodate the effects of censorship and missingness within a Bayesian framework. Specifically, we develop a Markov chain Monte Carlo procedure that combines a Gibbs sampler with the Metropolis-Hastings algorithm. This hybrid approach facilitates Bayesian estimation of essential model parameters and imputation of non-responses under the missing at random mechanism. The issue of posterior predictive inference for the censored and missing outcomes is also addressed. The effectiveness and performance of the proposed methodology are demonstrated through the analysis of simulated data and a real example from an AIDS clinical study.
dc.fuente.origenWOS
dc.identifier.doi10.1007/s00184-023-00929-x
dc.identifier.eissn1435-926X
dc.identifier.issn0026-1335
dc.identifier.urihttps://doi.org/10.1007/s00184-023-00929-x
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/75323
dc.identifier.wosidWOS:001090673900001
dc.information.autorucFacultad de Matemáticas; Castro Cepero, Luis Mauricio; 0000-0001-7249-5207; 151425
dc.language.isoen
dc.nota.accesoContenido parcial
dc.publisherSringer Heidelberg
dc.revistaMetrika
dc.rightsacceso restringido
dc.subjectCensored data recovery
dc.subjectMarkov chain Monte Carlo
dc.subjectMissing data imputation
dc.subjectPosterior sampling
dc.subjectTruncated multivariate normal distribution
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleBayesian multivariate nonlinear mixed models for censored longitudinal trajectories with non-monotone missing values
dc.typeartículo
sipa.codpersvinculados151425
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