Title: Exact simultaneous confidence intervals for logical selection of a biomarker cut-point

Abstract: Four new principles are proposed in this work for logical biomarker cut-point selection methods to adhere to: subgroup sensibility, sensitivity, specificity, and target monotonicity. At every cut-point value, our method gives confidence intervals not only for the efficacy at that cut-point value, but also efficacies in the marker-positive and marker-negative subgroups defined by that cut-point. These confidence intervals are given simultaneously for all possible cut-point values. Using Alzheimer’s disease and type 2 diabetes as examples, we show our method achieves the four principles. Our method strongly controls familywise type I error rate (FWER) across both levels of multiplicity: the multiplicity of having marker-positive and marker-negative subgroups at each cut-point, and the multiplicity of searching through infinitely many cut-points. This is in contrast to other available methods. The confidence level of our simultaneous confidence intervals is in fact exact (not conservative). An application (app) is available, which implements the method we propose.

简介：

韩杨，英国曼彻斯特大学数学系统计学副教授。主要研究方向是同时推断、多重比较方法和个性化医疗。她是英国高等教育学院会员和皇家统计学会会员，曾获得曼彻斯特大学年度教师杰出成就奖，在Technometrics，Journal of Computational and Graphical Statistics，Journal of the Royal Statistical Society: Series C Statistica Sinica等发表学术论文20余篇。

报告时间：2024年7月8号 10：00-11：00

地点： 统计与数据科学学院会议室106