报告人: Xinyuan Song (The Chinese University of Hong Kong)
报告时间: 5月8日 16:00-17:00
#腾讯会议：896-621-505
报告摘要: 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. A high-dimensional functional principal component analysis is adopted to extract the principal eigenimages to reduce the ultrahigh dimensionality of imaging data, and 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 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 than existing methods.
个人简介: Xinyuan Song is a full professor and Chair in the Department of Statistics, 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, causal inference, and statistical computing. She serves/served as an associate editor for multiple 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.
邀请人: 徐礼柏