学术报告（郭心舟 3.17）
Toward Interpretable and Efficient Inference on Subgroups with Subgroup Size and Subgroup Effect Relationship
Abstract:
Subgroup analysis is frequently used to uncover and confirm treatment effect heterogeneity in clinical trials. When multiple candidate subgroups are considered, we often need to make statistical inference on the subgroups simultaneously. Classical multiple testing procedures might suffer from the loss of interpretability and efficiency as they often fail to take subgroup size and subgroup effect relationship into account. In this talk, built on the selective traversed accumulation rules (STAR), we propose a data-adaptive and interactive multiple testing procedure to account for subgroup size and subgroup effect relationship for tree-structured subgroups. The proposed method is easy-to-implement and can lead to a more efficient and interpretable inference on tree-structured subgroups. We demonstrate the merit of our proposed method by re-analyzing the panitumumab trial with the proposed method. This talk is based on joint work with Yuanhui Luo (HKUST).
Bio:
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.
