Joint Analysis of Mixed Types of Outcomes with Latent Variables

发布时间：2023-07-10 作者：浏览次数：

Speaker:	潘灯	DateTime:	2023年7月11日（周二）下午4:00-5:00
Brief Introduction to Speaker: 潘灯，华中科技大学数学与统计学院副教授，毕业于香港中文大学。研究方向为结构方程模型、贝叶斯统计以及生存分析等。		Place:	6号楼4楼会议室
Abstract: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 s...