时间：2019年12月23日（周一）16:00-17:00
地点：中北校区理科大楼A1716
题目：A unified statistical learning framework for the minimal clinically important difference
报告人：赵继伟，美国纽约州立大学布法罗分校 教授
主持人：方方 副教授
摘要：
The minimal clinically important difference (MCID), the smallest change in a treatment outcome that an individual patient would identify as important and which would indicate a change in the patient’s management, has been a fundamentally critical concept in personalized medicine and population health for decades. However, most of the currently existing methods of determining the MCID are ad hoc, and cannot incorporate the covariate factors emerged dramatically as the use of the electronic health records. In this talk, we propose a principled, unified statistical learning framework of estimating the MCID. We consider both the traditional low-dimensional and the practical high-dimensional cases pertaining to the covariate factors. We contrast the difference of both situations theoretically and conduct comprehensive simulation studies to reinforce these theoretical findings. We also apply our method to the study of chondral lesions in knee surgery to demonstrate the usefulness of the proposed approach.
报告人简介：
Jiwei Zhao, PhD. Jiwei has been an Assistant Professor at the State University of New York at Buffalo since 2014. Prior to that, he earned his PhD in Statistics from the University of Wisconsin-Madison in 2012. He has mainly working on nonignorable missing data and high-dimensional data over the past few years. He has expertise in semiparametric statistics, unconventional likelihoods, and statistical machine learning tools. He has thus far successfully published over 30 peer-reviewed papers in statistical methodology and interdisciplinary research.