Bayesian first order auto-regressive latent variable models for multiple binary sequences

Simple item page
dc.contributor.authorGiardina, Federica
dc.contributor.authorGuglielmi, Alessandra
dc.contributor.authorQuintana, Fernando A.
dc.contributor.authorRuggeri, Fabrizio
dc.date.accessioned2024-01-10T13:11:25Z
dc.date.available2024-01-10T13:11:25Z
dc.date.issued2011
dc.description.abstractLongitudinal clinical trials often collect long sequences of binary data monitoring a disease process over time. Our application is a medical study conducted in the US by the Veterans Administration Cooperative Urological Research Group to assess the effectiveness of a chemotherapy treatment (thiotepa) in preventing recurrence on subjects affected by bladder cancer. We propose a generalized linear model with latent auto-regressive structure for longitudinal binary data following a Bayesian approach. We discuss inference as well as sensitivity to prior choices for the bladder cancer data. We find that there is a significant treatment effect in the sense that treated patients have much smaller predicted recurrence probabilities than placebo patients.
dc.description.funderMIUR-Italy
dc.description.funderFONDECYT
dc.description.funderLaboratorio de Analisis Estocastico
dc.fechaingreso.objetodigital2024-05-14
dc.format.extent18 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1177/1471082X1001100601
dc.identifier.issn1471-082X
dc.identifier.urihttps://doi.org/10.1177/1471082X1001100601
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/78042
dc.identifier.wosidWOS:000298353300001
dc.information.autorucMatemática;Quintana F;S/I;100343
dc.issue.numero6
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final488
dc.pagina.inicio471
dc.publisherSAGE PUBLICATIONS LTD
dc.revistaSTATISTICAL MODELLING
dc.rightsacceso restringido
dc.subjectbinary longitudinal data
dc.subjectfirst order auto-regressive model
dc.subjecthierarchical Bayesian modelling
dc.subjectlatent variables
dc.subjectNONPARAMETRIC METHODS
dc.subjectPROBIT MODELS
dc.subjectPARAMETERS
dc.subjectREGRESSION
dc.subjectORDER
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleBayesian first order auto-regressive latent variable models for multiple binary sequences
dc.typeartículo
dc.volumen11
sipa.codpersvinculados100343
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
2024-05-14. Bayesian first order auto-regressive latent variable models for multiple binary sequences.pdf
Size:
2.58 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Artículos de revistas