Childhood obesity in Singapore: A Bayesian nonparametric approach

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dc.contributor.authorBeraha, Mario
dc.contributor.authorGuglielmi, Alessandra
dc.contributor.authorQuintana, Fernando Andres
dc.contributor.authorDe Iorio, Maria
dc.contributor.authorEriksson, Johan Gunnar
dc.contributor.authorYap, Fabian
dc.date.accessioned2025-01-20T17:11:44Z
dc.date.available2025-01-20T17:11:44Z
dc.date.issued2024
dc.description.abstractOverweight and obesity in adults are known to be associated with increased risk of metabolic and cardiovascular diseases. Obesity has now reached epidemic proportions, increasingly affecting children. Therefore, it is important to understand if this condition persists from early life to childhood and if different patterns can be detected to inform intervention policies. Our motivating application is a study of temporal patterns of obesity in children from South Eastern Asia. Our main focus is on clustering obesity patterns after adjusting for the effect of baseline information. Specifically, we consider a joint model for height and weight over time. Measurements are taken every six months from birth. To allow for data-driven clustering of trajectories, we assume a vector autoregressive sampling model with a dependent logit stick-breaking prior. Simulation studies show good performance of the proposed model to capture overall growth patterns, as compared to other alternatives. We also fit the model to the motivating dataset, and discuss the results, in particular highlighting cluster differences. We have found four large clusters, corresponding to children sub-groups, though two of them are similar in terms of both height and weight at each time point. We provide interpretation of these clusters in terms of combinations of predictors.
dc.fuente.origenWOS
dc.identifier.doi10.1177/1471082X231185892
dc.identifier.eissn1477-0342
dc.identifier.issn1471-082X
dc.identifier.urihttps://doi.org/10.1177/1471082X231185892
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/91204
dc.identifier.wosidWOS:001068953300001
dc.issue.numero6
dc.language.isoen
dc.pagina.final560
dc.pagina.inicio541
dc.revistaStatistical modelling
dc.rightsacceso restringido
dc.subjectclustering
dc.subjectlongitudinal profiles
dc.subjectobesity development
dc.subjectcovariate dependent priors
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleChildhood obesity in Singapore: A Bayesian nonparametric approach
dc.typeartículo
dc.volumen24
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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