Article first published online: 4 OCT 2011
Copyright © 2011 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 31, Issue 2, pages 188–200, 30 January 2012
How to Cite
Wang, Y.-F. and Fan, T.-H. (2012), Bayesian analysis of the structural equation models with application to a longitudinal myopia trial. Statist. Med., 31: 188–200. doi: 10.1002/sim.4378
- Issue published online: 28 DEC 2011
- Article first published online: 4 OCT 2011
- Manuscript Accepted: 1 AUG 2011
- Manuscript Received: 29 MAY 2010
- National Science Council of Taiwan. Grant Number: NSC98-2112-2118-005-MY2
- structural equation model;
- longitudinal data;
- latent variables;
- posterior predictive p-values
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.