報告題目：Uncertainty Quantification of Treatment Regime in Precision Medicine with an Application in Nonparametric Adaptive Design
報 告 人：Min-ge Xie 教授 美國羅格斯大學
報告時間：2019年7月10日下午3:10-3:50
報告地點：數學樓一樓第一報告廳
報告摘要:
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)
報告人簡介：
Min-ge Xie是美國羅格斯大學統計系的傑出教授，同時也是美國羅格斯大學統計咨詢辦公室的主任。他的主要研究興趣在于彌合統計推斷的基礎，并為跨學科研究産生的問題開發新的統計方法和理論。他的研究興趣還包括生物醫學科學，社會科學，工業，工程和環境科學的統計應用。他獲得過中國科學技術大學（USTC）的數學學士學位和伊利諾伊大學厄巴納 - 香槟分校（UIUC）的統計學碩士和博士學位。他曾參與過由國家科學基金會（NSF），國家衛生研究院（NIH），退伍軍人事務部（VA），聯邦航空管理局等機構資助的研究項目。
