Song Xin-Yuan, Lee Sik-Yum, Ng Maggie C Y, So Wing-Yee, Chan Juliana C N
Department of Statistics, The Chinese University of Hong Kong, Shatin NT, Hong Kong.
Stat Med. 2007 May 20;26(11):2348-69. doi: 10.1002/sim.2713.
There is now increasing evidence proving that many complex diseases can be significantly influenced by correlated phenotype and genotype variables, as well as their interactions. Effective and rigorous assessment of such influence is difficult, because the number of phenotype and genotype variables of interest may not be small, and a genotype variable is an unordered categorical variable that follows a multinomial distribution. To address the problem, we establish a novel nonlinear structural equation model for analysing mixed continuous and multinomial data that can be missing at random. A confirmatory factor analysis model with Kronecker product is proposed for grouping the manifest continuous and multinomial variables into latent variables according to their functions; and a nonlinear structural equation is formulated to assess the linear and interaction effects of the independent latent variables to the dependent latent variables. Bayesian methods for estimation and model comparison are developed through Markov chain Monte Carlo techniques and path sampling. The newly developed methodologies are applied to a case-control cohort of type 2 diabetic patients with nephropathy.
现在有越来越多的证据表明，许多复杂疾病会受到相关表型和基因型变量及其相互作用的显著影响。对此类影响进行有效且严格的评估很困难，因为感兴趣的表型和基因型变量数量可能不少，而且基因型变量是一个无序分类变量，服从多项分布。为解决该问题，我们建立了一种新颖的非线性结构方程模型，用于分析可能随机缺失的混合连续和多项数据。提出了一种带有克罗内克积的验证性因子分析模型，以便根据其功能将显性连续和多项变量分组为潜在变量；并构建了一个非线性结构方程，以评估独立潜在变量对相关潜在变量的线性和交互作用。通过马尔可夫链蒙特卡罗技术和路径抽样开发了用于估计和模型比较的贝叶斯方法。新开发的方法应用于2型糖尿病肾病患者的病例对照队列。
