学术报告报告主题: Data-Driven Subgroup Analysis: From Targeting, Identification to Evaluation
报 告 人: 郭心舟
报告时间：2026年3月23日（星期一）下午15:00-16:00
报告地点：清清文理楼3A02
邀 请 人: 张家睿副教授
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数学学院
2026年3月4日
报告摘要：
Subgroup analysis refers to the analysis of treatment effects within subpopulations and is frequently used in drug development and regulatory decision-making. In many modern biomedical studies, we lack sufficient prior knowledge and have to rely on data to segment the population. How to conduct valid and efficient data-driven subgroup analysis is a critical question bearing both methodological and practical importance. In this talk, we review the usual pipeline of data-driven subgroup analysis, starting from targeting, identification to evaluation of subgroups, and discuss the challenges and possible solutions of the usual pipeline. In specific, we will study how to (1) appropriately target subgroups via debiased approach, (2) optimally and consistently identify the targeted subgroups via shift-based method and (3) validly and efficiently evaluate the identified subgroups via conditional perturbation. Real data applications of the proposed pipeline will be presented during the talk. In the end, we will give an introduction of the MATH PhD program at HKUST.
报告人介绍：
Xinzhou Guo is an Assistant Professor in the Department of Mathematics at the Hong Kong University of Science and Technology. He received his B.S. in Applied Mathematics from Peking University and Ph.D. in Statistics from the University of Michigan. Prior to joining HKUST in 2021, he did a postdoc at Harvard University. His main research interests are subgroup analysis, resampling methods, precision medicine and regulatory decision-making.