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

Authors:
S. K. Ng;K. K. W. Yau;A. H. Lee
Affiliations:
Department of Mathematics, University of Queensland Brisbane, QLD 4072, Australia;Department of Management Sciences, City University of Hong Kong Kowloon, Hong Kong;Department of Epidemiology and Biostatistics Curtin University of Technology Perth, WA 6845, Australia
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2003

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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 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.