报告人：SONG XINYUAN（香港中文大学）

报告题目：Joint analysis of multivariate longitudinal, imaging, and time-to-event data

报告摘要：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 Carlo techniques to perform estimation. The application to the Alzheimer’s Disease Neuroimaging Initiative dataset sheds new insight into the pathology of AD.

报告时间：2025年4月29日（星期二）14:00-15:00

报告地点：东三十二楼115室

邀请人：潘灯

报告人简介：宋心远，香港中文大学统计系教授，中国教育部授予的长江学者特聘教授、国际数理统计学会（IMS）会士、国际统计学会（ISI）当选会员。宋心远教授的研究方向包括潜变量模型、贝叶斯方法、生存分析、非参数和半参数方法以及统计计算等。宋心远教授担任Journal of the American Statistical Association，Journal of the Royal Statistical Society, Series A，Statistics in Medicine等多个国际期刊的副主编。近期论文主要发表于Journal of the American Statistical Association，Journal of the Royal Statistical Society, Series B，Biometrics，Annals of Applied Statistics等期刊。