报告题目：Concordance Matched Learning for Estimating Optimal Individualized Treatment Regimes
报 告 人：朱文圣 教授 东北师范大学
报告时间：2020年11月13日 14:00-15:00
报告地点：数学楼三楼天元研讨室6
校内联系人：朱复康 fzhu@jlu.edu.cn
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 optimal individualized treatment regime or individualized treatment rule is an important part of personalized medicine. 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 multicategorical treatment options in real-world settings. In this talk, we address this problem and propose a machine learning approach (CM-learning) to estimate optimal treatment regimes, which allows for more accurate assessment of individual treatment response and alleviation of confounding. More importantly, CM-leaning is doubly robust, efficient and easy to interpret. We first introduce the concordance-based value function that measures weighted concordance for each patient by matching imputation. We then find the optimal treatment regime to maximize the concordance-based value function through the use of tree structure that directly handles the problem of optimization with multicategorical treatment options. Through a large number of simulation studies, we demonstrate that CM-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，东北师范大学数学与统计学院教授、博士生导师、副经理。2006年博士毕业于东北师范大学，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。研究方向为生物统计与精准医疗，在JASA、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，入选吉林省第七批拔尖创新人才。
