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dc.contributor.authorLee, Andy H
dc.contributor.authorZhao, Yun
dc.contributor.authorYau, Kelvin KW
dc.contributor.authorNg, SK
dc.date.accessioned2017-05-03T15:26:09Z
dc.date.available2017-05-03T15:26:09Z
dc.date.issued2009
dc.date.modified2010-06-09T07:33:59Z
dc.identifier.issn0010-4825
dc.identifier.doi10.1016/j.compbiomed.2009.01.003
dc.identifier.urihttp://hdl.handle.net/10072/25424
dc.description.abstractRecurrent infections data are commonly encountered in medical research, where the recurrent events are characterised by an acute phase followed by a stable phase after the index episode. Two-component survival mixture models, in both proportional hazards and accelerated failure time settings, are presented as a flexible method of analysing such data. To account for the inherent dependency of the recurrent observations, random effects are incorporated within the conditional hazard function, in the manner of generalised linear mixed models. Assuming a Weibull or log-logistic baseline hazard in both mixture components of the survival mixture model, an EM algorithm is developed for the residual maximum quasi-likelihood estimation of fixed effect and variance component parameters. The methodology is implemented as a graphical user interface coded using Microsoft visual . Application to model recurrent urinary tract infections for elderly women is illustrated, where significant individual variations are evident at both acute and stable phases. The survival mixture methodology developed enable practitioners to identify pertinent risk factors affecting the recurrent times and to draw valid conclusions inferred from these correlated and heterogeneous survival data.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent465069 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier Science
dc.publisher.placeUnited Kingdom
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom301
dc.relation.ispartofpageto307
dc.relation.ispartofissue3
dc.relation.ispartofjournalComputers in Biology and Medicine
dc.relation.ispartofvolume39
dc.rights.retentionY
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchMedical and Health Sciences
dc.subject.fieldofresearchcode08
dc.subject.fieldofresearchcode09
dc.subject.fieldofresearchcode11
dc.titleA computer graphical user interface for survival mixture modeling of recurrent infections
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.rights.copyright© 2009 Elsevier. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
gro.date.issued2009
gro.hasfulltextFull Text
gro.griffith.authorNg, Shu Kay Angus


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