A Bayesian approach for mixed effects state-space models under skewness and heavy tails

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dc.contributor.authorHernandez-Velasco, Lina L.
dc.contributor.authorAbanto-Valle, Carlos A.
dc.contributor.authorDey, Dipak K.
dc.contributor.authorCastro, Luis M.
dc.date.accessioned2025-01-20T17:29:51Z
dc.date.available2025-01-20T17:29:51Z
dc.date.issued2023
dc.description.abstractHuman immunodeficiency virus (HIV) dynamics have been the focus of epidemiological and biostatistical research during the past decades to understand the progression of acquired immunodeficiency syndrome (AIDS) in the population. Although there are several approaches for modeling HIV dynamics, one of the most popular is based on Gaussian mixed-effects models because of its simplicity from the implementation and interpretation viewpoints. However, in some situations, Gaussian mixed-effects models cannot (a) capture serial correlation existing in longitudinal data, (b) deal with missing observations properly, and (c) accommodate skewness and heavy tails frequently presented in patients' profiles. For those cases, mixed-effects state-space models (MESSM) become a powerful tool for modeling correlated observations, including HIV dynamics, because of their flexibility in modeling the unobserved states and the observations in a simple way. Consequently, our proposal considers an MESSM where the observations' error distribution is a skew-t. This new approach is more flexible and can accommodate data sets exhibiting skewness and heavy tails. Under the Bayesian paradigm, an efficient Markov chain Monte Carlo algorithm is implemented. To evaluate the properties of the proposed models, we carried out some exciting simulation studies, including missing data in the generated data sets. Finally, we illustrate our approach with an application in the AIDS Clinical Trial Group Study 315 (ACTG-315) clinical trial data set.
dc.fuente.origenWOS
dc.identifier.doi10.1002/bimj.202100302
dc.identifier.eissn1521-4036
dc.identifier.issn0323-3847
dc.identifier.urihttps://doi.org/10.1002/bimj.202100302
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/91600
dc.identifier.wosidWOS:001086367900001
dc.issue.numero8
dc.language.isoen
dc.revistaBiometrical journal
dc.rightsacceso restringido
dc.subjectBayesian inference
dc.subjectheavy-tailed distribution
dc.subjectlongitudinal data
dc.subjectmixed-effects
dc.subjectskewness
dc.subjectstate-space models
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleA Bayesian approach for mixed effects state-space models under skewness and heavy tails
dc.typeartículo
dc.volumen65
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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