Document Detail


Estimating equations for a latent transition model with multiple discrete indicators.
MedLine Citation:
PMID:  11315015     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
B A Reboussin; K Y Liang; D M Reboussin
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Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, P.H.S.    
Journal Detail:
Title:  Biometrics     Volume:  55     ISSN:  0006-341X     ISO Abbreviation:  Biometrics     Publication Date:  1999 Sep 
Date Detail:
Created Date:  2001-04-20     Completed Date:  2001-08-16     Revised Date:  2007-11-14    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  839-45     Citation Subset:  IM    
Affiliation:
Department of Public Health Sciences, Section on Biostatistics, Wake Forest University School of Medicine, Winston-Salem, North Carolina 27157, USA. brebouss@wfubmc.edu
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MeSH Terms
Descriptor/Qualifier:
Aged
Aging
Biometry*
Data Interpretation, Statistical
Health Status Indicators
Humans
Likelihood Functions
Longitudinal Studies
Models, Statistical*
Grant Support
ID/Acronym/Agency:
AG14131/AG/NIA NIH HHS; GM49909/GM/NIGMS NIH HHS

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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