School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, China.
Department of Statistics, The Chinese University of Hong Kong, Shatin, Hong Kong.
Stat Med. 2021 Feb 28;40(5):1272-1284. doi: 10.1002/sim.8840. Epub 2020 Dec 9.
We propose a joint modeling approach to investigating the observed and latent risk factors of mixed types of outcomes. The proposed model comprises three parts. The first part is an exploratory factor analysis model that summarizes latent factors through multiple observed variables. The second part is a proportional hazards model that examines the observed and latent risk factors of multivariate time-to-event outcomes. The third part is a linear regression model that investigates the determinants of a continuous outcome. We develop a Bayesian approach coupled with MCMC methods to determine the number of latent factors, the association between latent and observed variables, and the important risk factors of different types of outcomes. A modified stochastic search item selection algorithm, which introduces normal-mixture-inverse gamma priors to factor loadings and regression coefficients, is developed for simultaneous model selection and parameter estimation. The proposed method is subjected to simulation studies for empirical performance assessment and then applied to a study concerning the risk factors of type 2 diabetes and the associated complications.
我们提出了一种联合建模方法，用于研究混合类型结局的观察和潜在风险因素。所提出的模型包括三个部分。第一部分是探索性因子分析模型，通过多个观察变量总结潜在因素。第二部分是比例风险模型，用于检查多变量时事件结局的观察和潜在风险因素。第三部分是线性回归模型，用于研究连续结局的决定因素。我们开发了一种贝叶斯方法，并结合 MCMC 方法，以确定潜在因素的数量、潜在变量和观察变量之间的关联，以及不同类型结局的重要风险因素。开发了一种改进的随机搜索项选择算法，该算法将正态混合逆伽马先验引入因子载荷和回归系数中，用于同时进行模型选择和参数估计。所提出的方法经过模拟研究以评估经验性能，然后应用于研究 2 型糖尿病的风险因素及其相关并发症。
