Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/43255
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dc.contributor.authorBrilleman, SL-
dc.contributor.authorCrowther, MJ-
dc.contributor.authorMoreno-Betancur, M-
dc.contributor.authorBuros Novik, J-
dc.contributor.authorDunyak, J-
dc.contributor.authorAl-Huniti, N-
dc.contributor.authorFox, R-
dc.contributor.authorHammerbacher, J-
dc.contributor.authorWolfe, R-
dc.date.accessioned2019-02-07T10:10:08Z-
dc.date.available2019-02-07T10:10:08Z-
dc.date.issued2018-10-31-
dc.identifier.citationStat Methods Med Res, 2018, pp. 962280218808821-?en
dc.identifier.urihttps://journals.sagepub.com/doi/10.1177/0962280218808821en
dc.identifier.urihttp://hdl.handle.net/2381/43255-
dc.description.abstractJoint modelling of longitudinal and time-to-event data has received much attention recently. Increasingly, extensions to standard joint modelling approaches are being proposed to handle complex data structures commonly encountered in applied research. In this paper, we propose a joint model for hierarchical longitudinal and time-to-event data. Our motivating application explores the association between tumor burden and progression-free survival in non-small cell lung cancer patients. We define tumor burden as a function of the sizes of target lesions clustered within a patient. Since a patient may have more than one lesion, and each lesion is tracked over time, the data have a three-level hierarchical structure: repeated measurements taken at time points (level 1) clustered within lesions (level 2) within patients (level 3). We jointly model the lesion-specific longitudinal trajectories and patient-specific risk of death or disease progression by specifying novel association structures that combine information across lower level clusters (e.g. lesions) into patient-level summaries (e.g. tumor burden). We provide user-friendly software for fitting the model under a Bayesian framework. Lastly, we discuss alternative situations in which additional clustering factor(s) occur at a level higher in the hierarchy than the patient-level, since this has implications for the model formulation.en
dc.description.sponsorshipThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: SLB is funded by an Australian National Health and Medical Research Council (NHMRC) Postgraduate Scholarship (ref: APP1093145), with additional support from an NHMRC Centre of Research Excellence grant (ref: 1035261) awarded to the Victorian Centre for Biostatistics (ViCBiostat). MJC is partly funded by a UK Medical Research Council (MRC) New Investigator Research Grant (ref: MR/P015433/1).en
dc.language.isoenen
dc.publisherSAGE Publicationsen
dc.relation.urihttps://www.ncbi.nlm.nih.gov/pubmed/30378472-
dc.rightsCopyright © 2018, The Authors. Deposited with reference to the publisher’s open access archiving policy. (http://www.rioxx.net/licenses/all-rights-reserved)en
dc.subjectLongitudinalen
dc.subjectcanceren
dc.subjecthierarchicalen
dc.subjectjoint modelen
dc.subjectmultilevelen
dc.subjectshared parameter modelen
dc.subjectsurvivalen
dc.titleJoint longitudinal and time-to-event models for multilevel hierarchical dataen
dc.typeJournal Articleen
dc.identifier.doi10.1177/0962280218808821-
dc.identifier.eissn1477-0334-
dc.description.statusPeer-revieweden
dc.description.versionPost-printen
dc.type.subtypeJournal Article-
pubs.organisational-group/Organisationen
pubs.organisational-group/Organisation/COLLEGE OF LIFE SCIENCESen
pubs.organisational-group/Organisation/COLLEGE OF LIFE SCIENCES/School of Medicineen
pubs.organisational-group/Organisation/COLLEGE OF LIFE SCIENCES/School of Medicine/Department of Health Sciencesen
Appears in Collections:Published Articles, Dept. of Health Sciences

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