Department of Statistics, Chinese University of Hong Kong, Shatin, NT, Hong Kong.
Stat Med. 2014 May 10;33(10):1723-37. doi: 10.1002/sim.6065. Epub 2013 Dec 15.
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.
肾脏疾病是糖尿病的常见并发症之一，尤其在亚洲人群中更为常见。此外，心血管疾病和肾脏疾病有共同的危险因素。本文提出了一种潜在变量模型，该模型具有潜在变量的非参数交互作用，用于基于香港糖尿病登记处的研究，该登记处于 1995 年成立，是香港威尔士亲王医院持续质量改进计划的一部分。肾脏结局（结果潜在变量）通过使用一系列未指定的单变量和双变量平滑函数制定的加法结构方程回归到心脏功能和糖尿病（解释潜在变量）。提出了贝叶斯 P-样条方法以及马尔可夫链蒙特卡罗算法来估计模型中的平滑函数、未知参数和潜在变量。通过模拟研究证明了所开发方法的性能。使用所提出的方法研究了心脏功能和糖尿病的非参数交互作用对肾脏结局的影响。
