dc.contributor.author | Ng, Shu Kay | |
dc.contributor.author | Tawiah, Richard | |
dc.contributor.author | Mclachlan, Geoffrey J | |
dc.contributor.author | Gopalan, Vinod | |
dc.date.accessioned | 2021-11-18T00:48:37Z | |
dc.date.available | 2021-11-18T00:48:37Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 1465-4644 | |
dc.identifier.doi | 10.1093/biostatistics/kxab037 | |
dc.identifier.uri | http://hdl.handle.net/10072/410196 | |
dc.description.abstract | Multimorbidity 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.peerreviewed | Yes | |
dc.language | eng | |
dc.publisher | Oxford University Press (OUP) | |
dc.relation.ispartofjournal | Biostatistics | |
dc.subject.fieldofresearch | Biostatistics | |
dc.subject.fieldofresearch | Genetics | |
dc.subject.fieldofresearch | Statistics | |
dc.subject.fieldofresearchcode | 490502 | |
dc.subject.fieldofresearchcode | 3105 | |
dc.subject.fieldofresearchcode | 4905 | |
dc.subject.keywords | Cancer registry data | |
dc.subject.keywords | Generalized linear mixed models | |
dc.subject.keywords | Informative censoring | |
dc.subject.keywords | Mean residual life | |
dc.subject.keywords | Multimorbidity | |
dc.title | Joint frailty modeling of time-to-event data to elicit the evolution pathway of events: a generalized linear mixed model approach | |
dc.type | Journal article | |
dc.type.description | C1 - Articles | |
dcterms.bibliographicCitation | Ng, 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.dateAccepted | 2021-10-06 | |
dcterms.license | http://creativecommons.org/licenses/by/4.0/ | |
dc.date.updated | 2021-11-16T22:37:07Z | |
dc.description.version | Version of Record (VoR) | |
gro.description.notepublic | This 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.hasfulltext | Full Text | |
gro.griffith.author | Gopalan, Vinod | |
gro.griffith.author | Ng, Shu Kay Angus | |