题目：Efficient Adjusted Joint Significance Test and Sobel-Type Confidence Interval for Mediation Effect

报告人：张海祥 副教授（天津大学）

摘要： Mediation analysis is an important statistical tool in many research fields. The joint significance test is widely utilized as a prominent statistical approach for examining mediation effects in practical applications. Nevertheless, the limitation of this mediation testing method stems from its conservative Type I error, which reduces its statistical power and imposes certain constraints on its popularity and utility. The proposed solution to address this gap is the adjusted joint significance test for one mediator, a novel data-adaptive test for mediation effect that exhibits significant advancements compared to traditional joint significance test. Additionally, a novel adjusted Sobel-type approach is proposed for the estimation of confidence intervals for the mediation effects, demonstrating significant advancements over conventional Sobel's confidence intervals. Our mediation testing and confidence intervals procedure is evaluated through comprehensive simulations, and compared with numerous existing approaches. Finally, we illustrate the usefulness of our method by analysing three real-world datasets with continuous, binary and time-to-event outcomes, respectively.

报告人简介：张海祥，天津大学应用数学中心副教授，博士毕业于吉林大学。 曾经在中国科学院和美国西北大学从事博士后科研工作。主要的研究方向为: 大数据统计推断, 高维中介分析。相关的成果发表在 Bioinformatics,  Journal of Computational and Graphical Statistics 等学术期刊。

  报告时间：5月28日 下午19:00-20:00

报告地点：腾讯会议：875-274-923

联系人：胡涛