报告题目：Toward Interpretable and Efficient Inference on Subgroups with Subgroup Size and Subgroup Effect Relationship

报告时间：2024-03-20  10:30-11:30

报 告 人：郭心舟 教授 (香港科技大学）

报告地点：东北楼四楼报告厅（404）

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).