Affordable Access

Estimating equations for a latent transition model with multiple discrete indicators.

Authors
  • Reboussin, B A1
  • Liang, K Y
  • Reboussin, D M
  • 1 Department of Public Health Sciences, Section on Biostatistics, Wake Forest University School of Medicine, Winston-Salem, North Carolina 27157, USA. [email protected]
Type
Published Article
Journal
Biometrics
Publication Date
September 1999
Volume
55
Issue
3
Pages
839–845
Identifiers
PMID: 11315015
Source
Medline
License
Unknown

Abstract

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.

Report this publication

Statistics

Seen <100 times