Joint analysis of multivariate longitudinal, imaging, and time-to-event data

发布时间：2025-04-22 作者：浏览次数：

Speaker:	宋心远	DateTime:	2025年4月29日（周二）上午10:00
Brief Introduction to Speaker: 香港中文大学统计学系宋心远教授		Place:	国交2号楼315会议室
Abstract:This study proposes a joint analysis of multivariate longitudinal data, survival data with a non-susceptible fraction, and ultrahigh-dimensional imaging data. The proposed model comprises three major components. The first component is a mixture proportional hazards cure model with images to examine the potential predictors of the non-susceptible probability and hazards of interest. The second component is a dynamic factor analysis model with images to characterize group-specific latent factors through multiple observed variables. The last component is a semiparametric trajectory model to reveal the change patterns of the dynamic latent factors in the “non-susceptible" and “susceptible” groups. A two-stage approach is developed for statistical inference. The first stage manages the imaging data through high-dimensional functional principal component analysis. The second stage develops a Bayesian approach coupled with penalized splines, data augmentation, and Markov chain Monte Car...