报告题目：Adaptive Increment Learning for Estimating Optimal Individualized Treatment Regimes

报告人： 朱文圣 教授 东北师范大学

照片：

邀请人：李本崇

报告时间：2020年8月23 日9:30—11:00

报告平台：腾讯会议：880 164 821

报告摘要：Personalized medicine has recently received increasing attention because of the significant heterogeneity of patient responses to the same medication. The estimation of individualized treatment regimes or individualized treatment rules is an important part of personalized medicine. Individualized treatment regimes are designed to recommend treatment decisions to patients based on their individual characteristics and to maximize the overall clinical benefit to the patients. However, most of the existing statistical methods are mainly focus on the estimation of optimal individualized decision rules for the two categories of treatment options and rely heavily on data from randomized controlled trials. There has been a relative lack of research work on the selection of multi-categorical treatment options in real-world settings. In this work, we address this challenge and propose a machine learning approach (AI-learning) to estimate optimal treatment regimes. This new learning approach allows for more accurate assessment of individual treatment response and alleviation of confounding. The increment value functions are proposed to compare matched pairs under a tree structure, and this approach allows for easy handling of the outcomes of various types of measuring treatment responses (including continuous, ordinal, and discrete outcomes). Through a large number of simulation studies, we demonstrate that AI-learning outperforms existing methods. Lastly, the proposed method is illustrated in an analysis of AIDS clinical trial data.

报告人简介：

朱文圣，东北师范大学williamhill威廉希尔官网教授、博士生导师、副经理。2006年12月博士毕业于东北师范大学，2013年12月起任东北师范大学williamhill威廉希尔官网教授。2008-2010年在耶鲁大学做博士后研究，2015-2017年访问北卡罗来纳大学教堂山分校。中国现场统计研究会计算统计分会副理事长、数据科学与人工智能分会秘书长，中国概率统计学会副秘书长，吉林省现场统计研究会秘书长等。主要从事统计学的方法与应用研究，在Journal of the American Statistical Association、Test、NeuroImage、中国科学等杂志发表学术论文多篇，主持并完成国家自然科学基金项目多项。