﻿ 数学学院、所2019年系列学术活动（第91场）：Min-ge Xie 教授 罗格斯大学-数学学院

### 数学学院、所2019年系列学术活动（第91场）：Min-ge Xie 教授 罗格斯大学

Personalized decision rule in precision medicine is a `discrete parameter’, for which theoretical development of statistical inference is lacking. This talk proposes a new way to quantify the estimation uncertainty in a personalized decision based on confidence distribution (CD). Suppose, in a regression setup, the optimal decision for treatment versus control for an individual z is determined by a linear decision rule D = I(m_1(z))>m_0(z)), where m_1(z) and m_0(z) are the expectations of potential outcomes of treatment and control, respectively. The estimated D has uncertainty. We propose to find a CD for v = m_1(z) – m_0(z) and compute a `confidence measure’ of the decision {D=1} = {v > 0}. This measure, with value in [0,1], provides a frequency-based assessment about the decision. For example, if the measure for {D=1} is 63%, then, out of 100 patients the same as patient z, 63 will benefit using treatment and 37 will be better off in control group. This confidence measure is shown to match well with the classical assessments of sensitivity and specificity, but without the need to know the true {D=1} or {D=0}. Utility of the development is demonstrated in an adaptive clinical trial with nonparametric regression models. Joint work with Yilei Zhan (Rutgers University) and Sijian Wang (Rutgers University)

Min-ge Xie是美国罗格斯大学统计系的杰出教授，同时也是美国罗格斯大学统计咨询办公室的主任。他的主要研究兴趣在于弥合统计推断的基础，并为跨学科研究产生的问题开发新的统计方法和理论。他的研究兴趣还包括生物医学科学，社会科学，工业，工程和环境科学的统计应用。他获得过中国科学技术大学（USTC）的数学学士学位和伊利诺伊大学厄巴纳 - 香槟分校（UIUC）的统计学硕士和博士学位。他曾参与过由国家科学基金会（NSF），国家卫生研究院（NIH），退伍军人事务部（VA），联邦航空管理局等机构资助的研究项目。