统计学系系列讲座之334期

时　间：2019年4月12日（星期五）10:00-11:00

地　点：史带楼501室

主持人：朱仲义教授 复旦大学管理学院统计学系

主　题：Tree and Forest-based Methods for Precision Medicine

主讲人：Professor Heping Zhang　Susan Dwight Bliss Professor of Biostatistics, Yale University School of Public Health

简　介：

Dr. Zhang published over 250 research articles and monographs in theory and applications of statistical methods and in several areas of biomedical research including epidemiology, genetics, child and women health, mental health, substance use, and reproductive medicine. He directed a training program in mental health research that was funded by the NIMH. He directs the Collaborative Center for Statistics in Science that coordinates the Reproductive Medicine Network to evaluate treatment effectiveness for infertility. He is a fellow of the American Statistical Association and a fellow of the Institute of Mathematical Statistics. He was named the 2008 Myrto Lefokopoulou distinguished lecturer by Harvard School of Public Health and a Medallion Lecturer by the Institute of Mathematical Statistics. In 2011, he received the Royan International Award on Reproductive Health.

摘　要：Double-blind, randomized clinical trials are the preferred approach to demonstrating the effectiveness of one treatment against another. The comparison is, however, made on the average group effects. While patients and clinicians have always struggled to understand why patients respond differently to the same treatment, and while much hope has been held for the nascent field of predictive biomarkers (e.g. genetic markers), there is still much utility in exploring whether it is possible to estimate treatment efficacy based on demographic and baseline variables including biomarkers. To address this issue, we introduce the concepts of the relative effectiveness of treatments and depth importance in tree and forest based methods. Our goal is to identify groups of patients that are more likely to respond one treatment than the other, in contrast to the tradition approach that searches for a superior treatment in a larger population. We consider outcome variables that can be continuous, binary, or censored.

This work includes contributions from Ms. Victoria Chen, Ph.D. candidate, Yale University.

 统计学系 

2019-4-8