Finite mixture regression model with random effects: application to neonatal hospital length of stay

被引:38
作者
Yau, KKW
Lee, AH
Ng, ASK
机构
[1] City Univ Hong Kong, Dept Management Sci, Hong Kong, Hong Kong, Peoples R China
[2] Curtin Univ Technol, Dept Epidemiol & Biostat, Perth, WA 6845, Australia
[3] Univ Queensland, Ctr Stat, Brisbane, Qld 4072, Australia
关键词
EM algorithm; generalised linear mixed models; heterogeneity; mixture distributions; random effects;
D O I
10.1016/S0167-9473(02)00180-9
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
A two-component mixture regression model that allows simultaneously for heterogeneity and dependency among observations is proposed. By specifying random effects explicitly in the linear predictor of the mixture probability and the mixture components, parameter estimation is achieved by maximising the corresponding best linear unbiased prediction type log-likelihood. Approximate residual maximum likelihood estimates are obtained via an EM algorithm in the manner of generalised linear mixed model (GLMM). The method can be extended to a g-component mixture regression model with the component density from the exponential family, leading to the development of the class of finite mixture GLMM. For illustration, the method is applied to analyse neonatal length of stay (LOS). It is shown that identification of pertinent factors that influence hospital LOS can provide important information for health care planning and resource allocation. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:359 / 366
页数:8
相关论文
共 14 条
[1]  
[Anonymous], 2000, WILEY SERIES PROBABI
[2]  
[Anonymous], SOCIOECON PLANN SCI, DOI DOI 10.1016/S0038-0121(98)00006-8
[3]   Determinants of maternity length of stay: A gamma mixture risk-adjusted model [J].
Lee A.H. ;
Ng A.S.K. ;
Yau K.K.W. .
Health Care Management Science, 2001, 4 (4) :249-255
[4]  
Lee AH, 1998, STAT MED, V17, P2199, DOI 10.1002/(SICI)1097-0258(19981015)17:19<2199::AID-SIM917>3.3.CO
[5]  
2-U
[6]   Hospital- and patient-related characteristics determining maternity length of stay: A hierarchical linear model approach [J].
Leung, KM ;
Elashoff, RM ;
Rees, KS ;
Hasan, MM ;
Legorreta, AP .
AMERICAN JOURNAL OF PUBLIC HEALTH, 1998, 88 (03) :377-381
[7]   Measuring performance in hospital care - Length of stay in gynaecology [J].
Leyland, AH ;
Boddy, FA .
EUROPEAN JOURNAL OF PUBLIC HEALTH, 1997, 7 (02) :136-143
[8]   Neonatal hospital lengths of stay, readmissions, and charges [J].
Marbella, AM ;
Chetty, VK ;
Layde, PM .
PEDIATRICS, 1998, 101 (01) :32-36
[9]  
MCGILCHRIST CA, 1994, J ROY STAT SOC B MET, V56, P61
[10]   THE DERIVATION OF BLUP, ML, REML ESTIMATION METHODS FOR GENERALIZED LINEAR MIXED MODELS [J].
MCGILCHRIST, CA ;
YAU, KKW .
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 1995, 24 (12) :2963-2980