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dc.contributor.authorNajera-Zuloaga J.en_US
dc.contributor.authorLee D.-J.en_US
dc.contributor.authorArostegui I.en_US
dc.date.accessioned2018-12-17T21:08:15Z
dc.date.available2018-12-17T21:08:15Z
dc.date.issued2018-05
dc.identifier.issn1521-4036
dc.identifier.urihttp://hdl.handle.net/20.500.11824/903
dc.description.abstractPatient-reported outcomes (PROs) are currently being increasingly used as primary outcome measures in observational and experimental studies since they inform clinicians and researchers about the health-status of patients and generate data to facilitate improved care. PROs usually appear as discrete and bounded with U, J or inverse J-shapes and hence, exponential family members offer inadequate distributional fits. The beta-binomial distribution has been proposed in the literature to fit PROs. However, the fact that the beta-binomial distribution does not belong to the exponential family limits its applicability in the regression model context, and classical estimation approaches are not straightforward. Moreover, PROs are usually measured in a longitudinal framework in which individuals are followed up for a certain period. Hence, each individual obtains several scores of the PRO over time, which leads to the repeated-measures and defines the correlation structure in the data. In this work, we have developed and proposed an estimation procedure for the analysis of correlated discrete and bounded outcomes, particularly PROs, by a beta-binomial mixed-effects model. Additionally, we have implemented the methodology in the PROreg package in R. Because there are similar approaches in the literature to address the same issue, this work also incorporates a comparison study between our proposal and alternative methodologies commonly implemented in R and shows the superior performance of our estimation procedure. This paper was motivated by the analysis of the health-status of patients with chronic obstructive pulmonary disease, where the main objective is the assessment of risk factors that may affect the evolution of the disease. The application of the proposed approach in the study leads to clinically relevant results.en_US
dc.description.sponsorshipDepartment of Education, Language Policy and Culture of the Basque Government IT-620-13 European Regional Development Funds (RD12/0001/0001)- through the thematic networks - REDISSEC (Red de Investigación en Servicios de Salud en Enfermedades Crónicas)en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.publisherBiometrical Journalen_US
dc.relationES/1PE/SEV-2017-0718en_US
dc.relationES/1PE/SEV-2013-0323en_US
dc.relationES/1PE/MTM2017-82379-Ren_US
dc.relationEUS/BERC/BERC.2018-2021en_US
dc.relationEUS/BERC/BERC.2014-2017en_US
dc.relationEUS/ELKARTEKen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectBeta-binomial distributionen_US
dc.subjectMixed-effects modelsen_US
dc.subjectPatient-reported outcomesen_US
dc.subjectPROregen_US
dc.subjectR-packageen_US
dc.titleA beta-binomial mixed-effects model approach for analysing longitudinal discrete and bounded outcomesen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typeinfo:eu-repo/semantics/acceptedVersionen_US
dc.identifier.doihttps://doi.org/10.1002/bimj.201700251
dc.relation.publisherversionhttps://onlinelibrary.wiley.com/doi/full/10.1002/bimj.201700251en_US


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