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

被引:1
|
作者
Wang, Yi-Fu [1 ]
Fan, Tsai-Hung [1 ]
机构
[1] Natl Cent Univ, Grad Inst Stat, Zhongli 320, Taiwan
关键词
structural equation model; longitudinal data; latent variables; MCMC; posterior predictive p-values; LATENT VARIABLE MODELS; REFRACTIVE ERRORS; HERITABILITY; ENVIRONMENT; GROWTH; GENES;
D O I
10.1002/sim.4378
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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 (C) 2011 John Wiley & Sons, Ltd.
引用
收藏
页码:188 / 200
页数:13
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