title: Latent variable models with nonparametric interaction effects of latent variables.
abstract: Renal disease is one of the common complications of diabetes, especially for Asian populations. Moreover, cardiovascular and renal diseases share common risk factors. This paper proposes a latent variable model with nonparametric interaction effects of latent variables for a study based on the Hong Kong Diabetes Registry, which was established in 1995 as part of a continuous quality improvement program at the Prince of Wales Hospital in Hong Kong. Renal outcome (outcome latent variable) is regressed in terms of cardiac function and diabetes (explanatory latent variables) through an additive structural equation formulated using a series of unspecified univariate and bivariate smooth functions. The Bayesian P-splines approach, along with a Markov chain Monte Carlo algorithm, is proposed to estimate smooth functions, unknown parameters, and latent variables in the model. The performance of the developed methodology is demonstrated via a simulation study. The effect of the nonparametric interaction of cardiac function and diabetes on renal outcome is investigated using the proposed methodology.
authors: Department of Statistics, Chinese University of Hong Kong, Shatin, NT, Hong Kong.
authors: Xinyuan
authors: X
authors: Song
authors: Zhaohua
authors: Z
authors: Lu
authors: Xiangnan
authors: X
authors: Feng
mesh_terms: D001499:Bayes Theorem; D001786:Blood Glucose / Q000378:metabolism; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006331:Heart Diseases / Q000150:complications; D006723:Hong Kong; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D051436:Renal Insufficiency, Chronic / Q000209:etiology
chemical_list: D001786:Blood Glucose
keywords: Bayesian P-splines; MCMC algorithm; latent variables; nonparametric interaction effects; semiparametric models