Bayesian analysis of the structural equation models with application to a longitudinal myopia trial

Myopia is becoming a significant public health problem, affecting more and more people. Studies indicate that there are two main factors, hereditary and environmental, suspected to have strong impact on myopia. Motivated by the increase in the number of people affected by this problem, this paper fo...

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Bibliographic Details
Published in:Statistics in medicine 2012-01, Vol.31 (2), p.188-200
Main Authors: Wang, Yi-Fu, Fan, Tsai-Hung
Format: Article
Language:English
Subjects:
Bayes Theorem
Bayesian analysis
Clinical trials
Environment
Humans
latent variables
longitudinal data
Longitudinal Studies
Mathematical models
MCMC
Medical statistics
Models, Statistical
Myopia
Myopia - epidemiology
Myopia - etiology
Myopia - genetics
posterior predictive p-values
Public health
structural equation model
Taiwan - epidemiology
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Summary:Myopia is becoming a significant public health problem, affecting more and more people. Studies indicate that there are two main factors, hereditary and environmental, suspected to have strong impact on myopia. Motivated by the increase in the number of people affected by this problem, this paper focuses primarily on the utilization of mathematical methods to gain further insight into their relationship with myopia. Accordingly, utilizing multidimensional longitudinal myopia data with correlation between both eyes, we develop a Bayesian structural equation model including random effects. With the aid of the MCMC method, it is capable of expressing the correlation between repeated measurements as well as the two‐eye correlation and can be used to explore the relational structure among the variables in the model. We consider four observed factors, including intraocular pressure, anterior chamber depth, lens thickness, and axial length. The results indicate that the genetic effect has much greater influence on myopia than the environmental effects. Copyright © 2011 John Wiley & Sons, Ltd.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.4378