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dc.contributor.authorNg, SK
dc.contributor.authorYau, KKW
dc.contributor.authorLee, AH
dc.date.accessioned2017-05-03T15:18:11Z
dc.date.available2017-05-03T15:18:11Z
dc.date.issued2003
dc.date.modified2010-08-16T06:47:32Z
dc.identifier.issn0895-7177
dc.identifier.doi10.1016/S0895-7177(03)00012-8
dc.identifier.urihttp://hdl.handle.net/10072/33468
dc.description.abstractThe modelling of inpatient length of stay (LOS) has important implications in health care studies. Finite mixture distributions are usually used to model the heterogeneous LOS distribution, due to a certain proportion of patients sustaining a longer stay. However, the morbidity data are collected from hospitals, observations clustered within the same hospital are often correlated. The generalized linear mixed model approach is adopted to accomodate the inherent correlation via unobservable random effects. An EM algorithm is developed to obtain residual maximum quasi-likelihood estimation. The proposed hierarchical mixture regression approach enables the identification and assessment of factors influencing the long-stay proportion and the LOS for the long-stay patient subgroup. A neonatal LOS data set is used for illustration.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherPergamon
dc.publisher.placeUK
dc.relation.ispartofpagefrom365
dc.relation.ispartofpageto375
dc.relation.ispartofissue3-4
dc.relation.ispartofjournalMathematical and Computer Modelling
dc.relation.ispartofvolume37
dc.subject.fieldofresearchApplied mathematics
dc.subject.fieldofresearchApplied mathematics not elsewhere classified
dc.subject.fieldofresearchNumerical and computational mathematics
dc.subject.fieldofresearchNumerical and computational mathematics not elsewhere classified
dc.subject.fieldofresearchTheory of computation
dc.subject.fieldofresearchTheory of computation not elsewhere classified
dc.subject.fieldofresearchcode4901
dc.subject.fieldofresearchcode490199
dc.subject.fieldofresearchcode4903
dc.subject.fieldofresearchcode490399
dc.subject.fieldofresearchcode4613
dc.subject.fieldofresearchcode461399
dc.titleModelling inpatient length of stay by a hierarchical mixture regression via the EM algorithm
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.date.issued2003
gro.hasfulltextNo Full Text
gro.griffith.authorNg, Shu Kay Angus


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