报告题目:Joint Mixed Membership Modeling of Multivariate Longitudinal and Survival Data

报告人：宋心远 教授 香港中文大学

报告时间： 2025年11月16日 15:30-16:30

报告地点：伍卓群楼三楼会议室

校内联系人：杜明月 [email protected]

报告摘要：In this article we present a novel joint mixed membership model for multivariate longitudinal AD-related biomarkers and time of AD diagnosis. Unlike conventional finite mixture models that assign each subject a single subgroup membership, the proposed model assigns partial membership across subgroups, allowing subjects to lie between two or more subgroups. This flexible structure enables individualized disease progression and facilitates the identification of clinically meaningful neurological statuses often elusive in current mixed effects models. We employ a spline-based trajectory model to characterize complex and possibly nonlinear patterns of multiple longitudinal clinical markers. A Cox model is then used to examine the effects of time-variant risk factors on the hazard of developing AD. We develop a Bayesian method coupled with efficient Markov chain Monte Carlo sampling schemes to perform statistical inference. The proposed approach is assessed through extensive simulation studies and an application to the Alzheimer’s Disease Neuroimaging Initiative study, showing a better performance in AD diagnosis than existing joint models.

报告人简介：

宋心远，香港中文大学统计学系教授，国际数理统计协会 (IMS) 会士以及国际统计协会 (ISI) Elected member。研究方向包括潜在变量模型、贝叶斯方法、纵向数据分析、生存分析和统计计算等。在JASA, JRSSB, Biometrics等国际期刊发表超过140篇论文。目前担任多个统计与心理测量学国际期刊的副主编，包括 JASA-A&CS、JRSSA、Statistics in Medicine、Electronic Journal of Statistics、Statistics and Its Interface、CSDA、Psychometrika 和 Structural Equation Modeling: A Multidisciplinary Journal等期刊。曾担任 Biometrics（2014–2016）和 The Canadian Journal of Statistics（2019-2021）的副主编。