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【学术报告】2021年7月5-6日宋心远教授举办系列学术讲座

作者: 时间:2021-07-02 点击量:

报告人:宋心远(香港中文大学)

邀请人:潘灯

报告人介绍:宋心远,香港中文大学统计系教授,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。

(一)

报告时间:2021年7月5日(星期一)16:00-18:00

报告地点:腾讯会议:658 9109 4353

报告题目:Causal Mediation Analysis with Latent Mediators and Survival Outcome

报告摘要:This study develops a joint modeling approach that incorporates latent traits into causal mediation analysis with multiple mediators and a survival outcome. A linear structural equation model is used to characterize the latent mediators with several highly correlated observable surrogates and depicts the relationships among multiple parallel or causally ordered mediators and the exposure. A proportional hazards model is used to derive the path-specific causal effects on the scale of hazard ratio under the counterfactual framework with a set of sequential ignorability assumptions. A Bayesian approach with Markov chain Monte Carlo algorithm is developed to perform efficient estimation of the causal effects. Posterior propriety theory is established for the proportional hazards model with latent variables. Empirical performance of the proposed method is verified through simulation studies. The proposed model is then applied to a study on the Alzheimer's Disease Neuroimaging Initiative dataset to investigate the causal effects of APOE-epsilon4 allele on the disease progression, either directly or through potential mediators, such as hippocampus atrophy, ventricle expansion, and cognitive impairment.


(二)

报告时间:2021年7月6日(星期二)16:00-18:00

报告地点:腾讯会议:658 9109 4353

报告题目:Mediation Analysis for Mixture Cox Proportional Hazards Cure Models

报告摘要:Mediation analysis aims to decompose a total effect into specific pathways and investigate the underlying causal mechanism. Although existing methods have been developed to conduct mediation analysis in the context of survival models, none of these methods accommodates the existence of a substantial proportion of subjects who never experience the event of interest, even if the follow-up is sufficiently long. In this study, we consider mediation analysis for mixture Cox proportional hazards cure models that cope with the cure fraction problem. Path-specific effects on restricted mean survival time and survival probability are assessed by introducing a partially latent group indicator and applying the mediation formula approach in a three-stage mediation framework. A Bayesian approach with P-splines for approximating the baseline hazard function is developed to conduct analysis. The satisfactory performance of the proposed method is verified through simulation studies. An application of the Alzheimer's Disease (AD) Neuroimaging Initiative dataset investigates the causal effects of APOE-epsilon4 allele on AD progression.


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