报告题目：Robust Covariate Balancing Method in Learning Optimal Individualized Treatment Regimes

报告人：朱文圣 教授 东北师范大学

报告时间：2024年5月26日（周日）10:30—12:00

报告地点：阜成路校区综合楼1116会议室

报告摘要: 

   Personalized medicine has recently received increasing attention since patients have heterogeneous responses to treatment. An important part of personalized medicine is individualized treatment regime (ITR), which helps medical practitioners to provide more precise treatment. That is, it can be designed to recommend treatment decisions to patients based on their individual characteristics and to maximize the overall clinical benefit to the patient. Most of the existing statistical methods usually assume an outcome regression model or a propensity score model to construct the value function. However, if any of the above assumptions are invalid, the estimated treatment regime is not reliable. In this article, we first define a contrast value function, which is the basis of the study for ITR. Then we construct a general framework of a hybrid estimator to estimate the contrast value function by combining two types of estimation methods. We further propose a covariate balancing robust (CBR) estimator of the contrast value function by combining the inverse probability weighted (IPW) method and matching method, which is based on Covariate Balancing Propensity Score (CBPS) proposed by Imai and Ratkovic (2014). The theoretical results show that the CBR estimator is doubly robust, that is, it is consistent if either the propensity score model or the matching is correct. Through a large number of simulation studies, we demonstrate that the CBR estimator 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4556银河国际在线教授、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。中国统计教育学会副会长，全国工业统计学教学研究会副会长。国家级一流本科课程《实用回归分析》负责人。在JASA, IEEE Transactions on Geoscience and Remote Sensing (TGRS), Statistica Sinica，Science China-Mathematics等杂志发表学术论文多篇，主持多项国家自然科学基金项目。