主题：Joint Modeling of Longitudinal Imaging and Survival Data
主讲人：宋心远 香港中文大学
主持人：王国长 暨南大学
时间：2024年4月26日（周五）下午15:30-16:30
地点：暨南大学石牌校区经济学院大楼（中惠楼）102室
摘要
This study considers a joint modeling framework for simultaneously examining the dynamic pattern of longitudinal and ultrahigh-dimensional images and their effects on the survival of interest. A functional mixed effects model is considered to describe the trajectories of longitudinal images. Then, a high-dimensional functional principal component analysis (HD-FPCA) is adopted to extract the principal eigenimages to reduce the ultrahigh dimensionality of imaging data. Finally, a Cox regression model is used to examine the effects of the longitudinal images and other risk factors on the hazard. A theoretical justification shows that a naive two-stage procedure that separately analyzes each part of the joint model produces biased estimation even if the longitudinal images have no measurement error. We develop a Bayesian joint estimation method coupled with efficient Markov chain Monte Carlo sampling schemes to perform statistical inference for the proposed joint model. A Monte Carlo dynamic prediction procedure is proposed to predict the future survival probabilities of subjects given their historical longitudinal images. The proposed model is assessed through extensive simulation studies and an application to Alzheimer's Disease Neuroimaging Initiative, which holds the promise of accuracy and possesses higher predictive capacity for survival outcomes compared with existing methods.
主讲人简介
Xinyuan Song is a full professor and chair of the Department of Statistics at the Chinese University of Hong Kong. She is currently a Changjiang Scholar Chair Professor, awarded by the Education Ministry of China. Her research interests are latent variable models, Bayesian methods, survival analysis, nonparametric and semiparametric methods, and statistical computing. She serves/served as an associate editor for several international journals in Statistics and Psychometrics, including Biometrics, Electronic Journal of Statistics, Canadian Journal of Statistics, Statistics and Its Interface, Computational Statistics and Data Analysis, Psychometrika, and Structural Equation Modeling: A Multidisciplinary Journal.
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校对|王国长
责编|彭毅
初审|姜云卢
终审发布|何凌云
（来源：暨南大学经济学院微信公众号）
