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dc.contributor.authorNg, Shu Kay
dc.contributor.authorTawiah, Richard
dc.contributor.authorMclachlan, Geoffrey J
dc.contributor.authorGopalan, Vinod
dc.date.accessioned2021-11-18T00:48:37Z
dc.date.available2021-11-18T00:48:37Z
dc.date.issued2021
dc.identifier.issn1465-4644
dc.identifier.doi10.1093/biostatistics/kxab037
dc.identifier.urihttp://hdl.handle.net/10072/410196
dc.description.abstractMultimorbidity constitutes a serious challenge on the healthcare systems in the world, due to its association with poorer health-related outcomes, more complex clinical management, increases in health service utilization and costs, but a decrease in productivity. However, to date, most evidence on multimorbidity is derived from cross-sectional studies that have limited capacity to understand the pathway of multimorbid conditions. In this article, we present an innovative perspective on analyzing longitudinal data within a statistical framework of survival analysis of time-to-event recurrent data. The proposed methodology is based on a joint frailty modeling approach with multivariate random effects to account for the heterogeneous risk of failure and the presence of informative censoring due to a terminal event. We develop a generalized linear mixed model method for the efficient estimation of parameters. We demonstrate the capacity of our approach using a real cancer registry data set on the multimorbidity of melanoma patients and document the relative performance of the proposed joint frailty model to the natural competitor of a standard frailty model via extensive simulation studies. Our new approach is timely to advance evidence-based knowledge to address increasingly complex needs related to multimorbidity and develop interventions that are most effective and viable to better help a large number of individuals with multiple conditions.
dc.description.peerreviewedYes
dc.languageeng
dc.publisherOxford University Press (OUP)
dc.relation.ispartofjournalBiostatistics
dc.subject.fieldofresearchBiostatistics
dc.subject.fieldofresearchGenetics
dc.subject.fieldofresearchStatistics
dc.subject.fieldofresearchcode490502
dc.subject.fieldofresearchcode3105
dc.subject.fieldofresearchcode4905
dc.subject.keywordsCancer registry data
dc.subject.keywordsGeneralized linear mixed models
dc.subject.keywordsInformative censoring
dc.subject.keywordsMean residual life
dc.subject.keywordsMultimorbidity
dc.titleJoint frailty modeling of time-to-event data to elicit the evolution pathway of events: a generalized linear mixed model approach
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationNg, SK; Tawiah, R; Mclachlan, GJ; Gopalan, V, Joint frailty modeling of time-to-event data to elicit the evolution pathway of events: a generalized linear mixed model approach., Biostatistics, 2021
dcterms.dateAccepted2021-10-06
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2021-11-16T22:37:07Z
dc.description.versionVersion of Record (VoR)
gro.description.notepublicThis publication has been entered as an advanced online version in Griffith Research Online.
gro.rights.copyright© The Author 2021. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorGopalan, Vinod
gro.griffith.authorNg, Shu Kay Angus
gro.griffith.authorTawiah, Richard


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