Semiparametric Bayesian inference for multilevel repeated measurement data

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dc.contributor.authorMuller, Peter
dc.contributor.authorQuintana, Fernando A.
dc.contributor.authorRosner, Gary L.
dc.date.accessioned2024-01-10T13:17:32Z
dc.date.available2024-01-10T13:17:32Z
dc.date.issued2007
dc.description.abstractWe discuss inference for data with repeated measurements at multiple levels. The motivating example is data with blood counts from cancer patients undergoing multiple cycles of chemotherapy, with days nested within cycles. Some inference questions relate to repeated measurements over days within cycle, while other questions are concerned with the dependence across cycles. When the desired inference relates to both levels of repetition, it becomes important to reflect the data structure in the model. We develop a semiparametric Bayesian modeling approach, restricting attention to two levels of repeated measurements. For the top-level longitudinal sampling model we use random effects to introduce the desired dependence across repeated measurements. We use a nonparametric prior for the random effects distribution. Inference about dependence across second-level repetition is implemented by the clustering implied in the nonparametric random effects model. Practical use of the model requires that the posterior distribution on the latent random effects be reasonably precise.
dc.description.funderNCI NIH HHS
dc.description.funderNATIONAL CANCER INSTITUTE
dc.fechaingreso.objetodigital2024-05-14
dc.format.extent10 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1111/j.1541-0420.2006.00668.x
dc.identifier.eissn1541-0420
dc.identifier.issn0006-341X
dc.identifier.pubmedidMEDLINE:17447954
dc.identifier.urihttps://doi.org/10.1111/j.1541-0420.2006.00668.x
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/78670
dc.identifier.wosidWOS:000244647100031
dc.information.autorucMatemática;Quintana F;S/I;100343
dc.issue.numero1
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final289
dc.pagina.inicio280
dc.publisherWILEY
dc.revistaBIOMETRICS
dc.rightsacceso restringido
dc.subjectBayesian nonparametrics
dc.subjectDirichlet process
dc.subjecthierarchical model
dc.subjectrepeated measurement data
dc.subjectDIRICHLET PROCESS MIXTURE
dc.subjectLINEAR MIXED MODELS
dc.subjectNONPARAMETRIC PROBLEMS
dc.subjectCLUSTERED DATA
dc.subjectREGRESSION
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleSemiparametric Bayesian inference for multilevel repeated measurement data
dc.typeartículo
dc.volumen63
sipa.codpersvinculados100343
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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