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

  • Yau K
  • Lee A
  • Ng A
  • 2

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Abstract

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.

Author-supplied keywords

  • EM algorithm
  • Generalised linear mixed models
  • Heterogeneity
  • Mixture distributions
  • Random effects

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Authors

  • Kelvin K W Yau

  • Andy H Lee

  • Angus S K Ng

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