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科学研究
RESEARCH
Concordance Matched Learning for Estimating Optimal Individualized Treatment Regimes
时间  Datetime
2020-10-27 14:00 — 15:00
地点  Venue
腾讯会议 APP()
报告人  Speaker
朱文圣
单位  Affiliation
东北师范大学
邀请人  Host
王成
备注  remarks
腾讯会议 ID:861 670 546 会议密码:654321
报告摘要  Abstract

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. 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 multicategorical treatment options in real-world settings. We address this problem and propose a machine learning approach (CM-learning) to estimate optimal treatment regimes. This new learning approach 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. Furthermore, an extension of CM-learning can be applied to ordinal treatment settings. 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.


报告人介绍:朱文圣,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。主要从事统计学的方法与应用研究,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。