Modelling inpatient length of stay by a hierarchical mixture regression via the EM algorithm

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Pages (from-to)365-375
Journal / PublicationMathematical and Computer Modelling
Volume37
Issue number3-4
Publication statusPublished - Mar 2003

Abstract

The 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 accommodate 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. © 2003 Elsevier Science Ltd. All rights reserved.

Research Area(s)

  • Clustered data, EM algorithm, Generalized linear mixed models, Mixture distribution, Random effects