Tang An-Min, Tang Nian-Sheng
Department of Statistics, Yunnan University, Kunming, Yunnan, 650091, China.
Stat Med. 2015 Feb 28;34(5):824-43. doi: 10.1002/sim.6373. Epub 2014 Nov 18.
We propose a semiparametric multivariate skew-normal joint model for multivariate longitudinal and multivariate survival data. One main feature of the posited model is that we relax the commonly used normality assumption for random effects and within-subject error by using a centered Dirichlet process prior to specify the random effects distribution and using a multivariate skew-normal distribution to specify the within-subject error distribution and model trajectory functions of longitudinal responses semiparametrically. A Bayesian approach is proposed to simultaneously obtain Bayesian estimates of unknown parameters, random effects and nonparametric functions by combining the Gibbs sampler and the Metropolis-Hastings algorithm. Particularly, a Bayesian local influence approach is developed to assess the effect of minor perturbations to within-subject measurement error and random effects. Several simulation studies and an example are presented to illustrate the proposed methodologies.
我们提出了一种用于多变量纵向和多变量生存数据的半参数多变量偏态正态联合模型。所假定模型的一个主要特征是，我们通过使用中心狄利克雷过程先验来指定随机效应分布，并使用多变量偏态正态分布来指定个体内误差分布以及半参数化纵向反应的模型轨迹函数，从而放宽了对随机效应和个体内误差常用的正态性假设。提出了一种贝叶斯方法，通过结合吉布斯采样器和梅特罗波利斯-黑斯廷斯算法，同时获得未知参数、随机效应和非参数函数的贝叶斯估计。特别地，开发了一种贝叶斯局部影响方法来评估个体内测量误差和随机效应的微小扰动的影响。给出了几个模拟研究和一个实例来说明所提出的方法。
