Estimating equations for a latent transition model with multiple discrete indicators

Beth A. Reboussin, Kung Yee Liang, David M. Reboussin

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


This paper proposes a two-part model for studying transitions between health states over time when multiple, discrete health indicators are available. The includes a measurement model positing underlying latent health states and a transition model between latent health states over time. Full maximum likelihood estimation procedures are computationally complex in this latent variable framework, making only a limited class of models feasible and estimation of standard errors problematic. For this reason, an estimating equations analogue of the pseudo-likelihood method for the parameters of interest, namely the transition model parameters, is considered. The finite sample properties of the proposed procedure are investigated through a simulation study and the importance of choosing strong indicators of the latent variable is demonstrated. The applicability of the methodology is illustrated with health survey data measuring disability in the elderly from the Longitudinal Study of Aging.

Original languageEnglish (US)
Pages (from-to)839-845
Number of pages7
Issue number3
StatePublished - Sep 1999


  • Estimating equations
  • Latent transition models
  • Multivariate categorical data

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics


Dive into the research topics of 'Estimating equations for a latent transition model with multiple discrete indicators'. Together they form a unique fingerprint.

Cite this