时 间：2023年4月21日（周五）15：00
地 点: 腾讯会议：260238347
报告人：朱文圣 东北师范大学教授
主持人：谌自奇 研究员
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 patient. However, most of the existing statistical methods are mainly concerned with 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 multicategory treatment options in real-world settings. We address this challenge and propose a machine learning approach (ACML) to estimate optimal multi-category treatment regimes. This new learning approach allows for more accurate assessment of individual treatment response and alleviation of confounding, more importantly, ACML 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 propose a novel surrogate loss and employ an angle-based method to maximize the concordance-based value function that directly handles the problem of optimization with multicategory treatment options. Furthermore, an extension of ACML can be applied to ordinal treatment settings. The theoretical results show that proposed method is doubly robust. We further obtain that the resulting estimator of the treatment rule is consistent. Through a large number of simulation studies, we demonstrate that ACML 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。全国工业统计学教学研究会副会长、中国现场统计研究会数据科学与人工智能分会秘书长等。研究兴趣有生物统计学、精准医疗的统计推断、统计机器学习等。在Journal of the American Statistical Association (JASA), IEEE Transactions on Geoscience and Remote Sensing (TGRS), Statistica Sinica, Scandinavian Journal of Statistics等杂志发表学术论文多篇，主持多项国家自然科学基金面上项目。