2020年11月17日下午16：30分在南湖校区教学科研楼609室，东北师范大学朱文圣教授莅临我院做学术报告，会议由学院副院长徐平峰主持，学院部分老师、研究生参加了本次学术报告会。
报告题目：Concordance Matched Learning for Estimating Optimal Individualized Treatment Regimes
摘要：Precision medicine has drawn tremendous attention recently to account for significant heterogeneity in the response of different patients to the same treatment. The estimation of the optimal individualized treatment regime (ITR) is of great concern to precision medicine, which is aim to recommend a treatment regime based on patient-specific characteristics by maximizing the expected clinical outcome. In recent statistical literatures, there is a large and growing body of different statistical methods to estimate optimal individualized treatment regimes. 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. In this talk, we propose a machine learning approach (CM-learning) to estimate optimal treatment regime from multicategorical treatment options, 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. 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.
朱文圣简介：东北师范大学数学与统计学院教授、博士生导师、副院长。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。
在报告会中，朱文圣教授以“Concordance Matched Learning for EstimatingOptimal Individualized Treatment Regimes”为题，以诙谐幽默的语言向我们讲述了一致性匹配学习估计最佳的个体治疗方案。提出了新的具有建设性开创性的观点。整场报告深入浅出、旁征博引，实例鲜活，诙谐幽默，引起了与会师生们的热烈反响。
报告结束后，场下开始了精彩的讨论，教授和同学们也都分享了自己的见解，就刚才的知识点以及自己的疑难点向教授提出了疑问，教授们一一耐心作答。在场的成员都觉得受益匪浅。
本次学术报告立意深远，思想深邃，实例丰富，内容精彩，为学术们在后续的学习提供了更好的知识储备，夯实了理论基础。激励了同学们在以后的学习中能有更加深入的研究，为今后的学习提供了更多思路。
数学与统计学院
2020年11月17日
