Estimating Equations for a Latent Transition Model with Multiple Discrete Indicators

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...

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Bibliographic Details
Published in:Biometrics 1999-09, Vol.55 (3), p.839-845
Main Authors: Reboussin, Beth A., Liang, Kung-Yee, Reboussin, David M.
Format: Article
Language:English
Subjects:
Aged
Aging
Biometry
Consistent estimators
Covariance
Data Interpretation, Statistical
Disabilities
Estimation bias
Health indicators
Health Status Indicators
Humans
Likelihood Functions
Longitudinal Studies
Mathematical independent variables
Maximum likelihood estimation
Models, Statistical
Older adults
Sampling bias
Standard error
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Summary: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.
ISSN:0006-341X
1541-0420
DOI:10.1111/j.0006-341X.1999.00839.x