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2019-12-26 11:53:30    浏览次数:

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报告人:Song Xinyuan 宋心远 教授(香港中文大学)

报告题目:Joint Modeling of Longitudinal Imaging and Survival Data

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.  Then, a high-dimensional functional principal component analysis (HD-FPCA) is adopted to extract the principal eigenimages to reduce the ultrahigh dimensionality of the imaging data. Finally, a Cox regression model is used to examine the effects of the longitudinal images and other covariates on the hazards of interest.  A theoretical justification shows that a naive two-stage procedure that separately analyzes each part of the joint model produces biased estimation. 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. Moreover, a Monte Carlo dynamic prediction procedure is proposed to predict the survival probabilities of future subjects given their historical longitudinal images. The proposed method is assessed through simulation studies and applied to the study of Alzheimer's Disease Neuroimaging Initiative. New insights into the early diagnosis and prevention of Alzheimer's disease are obtained.

报告人概况:宋心远,香港中文大学统计系主任。宋心远教授的研究方向是潜变量模型,贝叶斯方法,统计计算和生存分析等。同时还担任多个国际期刊包括《Psychometrika》,《Biometrics》,《Computational Statistics & Data Analysis》和《Structural Equation Modeling: A Multidisciplinary Journal》的副主编或编委。已在国际期刊发表超过100篇论文,近期论文主要发表于《Journal of the American Statistical Association》,《Biometrika》,《Biometrics》,《Bioinformatics》,《Psychometrika》,《Quantitative Finance》等期刊。

报告时间:2019年12月26日(星期四)下午3:00 - 5:00


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