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dc.contributor.authorTawiah, Richard
dc.contributor.authorMcLachlan, Geoffrey
dc.contributor.authorNg, Shu Kay
dc.date.accessioned2020-03-17T01:24:33Z
dc.date.available2020-03-17T01:24:33Z
dc.date.issued2019
dc.identifier.issn0962-2802
dc.identifier.doi10.1177/0962280219859377
dc.identifier.urihttp://hdl.handle.net/10072/392357
dc.description.abstractMany medical studies yield data on recurrent clinical events from populations which consist of a proportion of cured patients in the presence of those who experience the event at several times (uncured). A frailty mixture cure model has recently been postulated for such data, with an assumption that the random subject effect (frailty) of each uncured patient is constant across successive gap times between recurrent events. We propose two new models in a more general setting, assuming a multivariate time-varying frailty with an AR(1) correlation structure for each uncured patient and addressing multilevel recurrent event data originated from multi-institutional (multi-centre) clinical trials, using extra random effect terms to adjust for institution effect and treatment-by-institution interaction. To solve the difficulties in parameter estimation due to these highly complex correlation structures, we develop an efficient estimation procedure via an EM-type algorithm based on residual maximum likelihood (REML) through the generalised linear mixed model (GLMM) methodology. Simulation studies are presented to assess the performances of the models. Data sets from a colorectal cancer study and rhDNase multi-institutional clinical trial were analyzed to exemplify the proposed models. The results demonstrate a large positive AR(1) correlation among frailties across successive gap times, indicating a constant frailty may not be realistic in some situations. Comparisons of findings with existing frailty models are discussed.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSAGE Publications
dc.relation.ispartofjournalStatistical Methods in Medical Research
dc.subject.fieldofresearchStatistics
dc.subject.fieldofresearchPublic Health and Health Services
dc.subject.fieldofresearchcode0104
dc.subject.fieldofresearchcode1117
dc.subject.keywordsScience & Technology
dc.subject.keywordsLife Sciences & Biomedicine
dc.subject.keywordsPhysical Sciences
dc.subject.keywordsHealth Care Sciences & Services
dc.subject.keywordsMathematical & Computational Biology
dc.titleMixture cure models with time-varying and multilevel frailties for recurrent event data
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationTawiah, R; McLachlan, G; Ng, SK, Mixture cure models with time-varying and multilevel frailties for recurrent event data, Statistical Methods in Medical Research, 2019
dc.date.updated2020-03-17T01:21:26Z
gro.description.notepublicThis publication has been entered into Griffith Research Online as an Advanced Online Version.
gro.hasfulltextNo Full Text
gro.griffith.authorTawiah, Richard
gro.griffith.authorNg, Shu Kay Angus


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