Shenzhen Reseach Institute and Department of Statistics, The Chinese University of Hong Kong, Hong Kong.
School of Mathematics and Statistics, Changchun University of Technology, Changchun, China.
Stat Med. 2018 Dec 10;37(28):4279-4297. doi: 10.1002/sim.7916. Epub 2018 Aug 13.
In this paper, we propose a semiparametric failure time model to analyze multivariate censored data with latent variables. The proposed model generalizes the conventional accelerated failure time model to accommodate latent risk factors that could be measured by multiple observed variables through a factor analysis and to incorporate additive nonparametric functions of observed and latent risk factors to examine their functional effects on multivariate failure times of interest. A Bayesian approach, along with Bayesian P-splines and Markov chain Monte Carlo techniques, is developed to estimate the unknown parameters and functions. The empirical performance of the proposed methodology is evaluated by a simulation study. An application to a study on the risk factors of two diabetes complications is presented.
本文提出了一种半参数失效时间模型，用于分析具有潜在变量的多元删失数据。所提出的模型将传统的加速失效时间模型推广到可以通过因子分析用多个观测变量来测量潜在风险因素，并纳入观测和潜在风险因素的加性非参数函数，以研究它们对多元失效时间的功能影响。采用贝叶斯方法，结合贝叶斯 P-样条和马尔可夫链蒙特卡罗技术来估计未知参数和函数。通过模拟研究评估了所提出方法的经验性能。本文还将该方法应用于一项关于 2 型糖尿病并发症风险因素的研究。
