主题：Inference on Potentially Identified Subgroups in Clinical Trials
主讲人：郭心舟 香港科技大学
主持人：王国长 88858cc永利官网
时间：2024年11月28日（周四）下午16:40-17:40
地点：88858cc永利官网石牌校区88858cc永利官网大楼（中惠楼）106室
摘要
When subgroup analyses are conducted in clinical trials with moderate or high dimensional covariates, we often need to identify candidate subgroups from the data and evaluate the potentially identified subgroups in a replicable way. The classical statistical inference applied to the potentially identified subgroups, assuming the subgroups are the same as what we observe from the data, might suffer from bias issue when the regularity assumption that the boundaries of the subgroups are negligible is violated. In this talk, we will introduce a shift-based method to address nonregularity bias issue and combining it with cross-fitting and subsampling, develop a de-biased inference procedure for potentially identified subgroups. The proposed method is model-free and asymptotically efficient whenever it is possible, and can be viewed as an asymmetric smoothing approach. The merits of the proposed method are demonstrated by re-analyzing the ACTG 175 trial. This talk is based on joint work with Shuoxun Xu.
主讲人简介
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.
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校对| 王国长
责编| 彭 毅
初审| 姜云卢
终审发布| 何凌云
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