题 目：Learning Individualized Treatment Rules with Many Treatments
报告人：刘玉峰 教授
主持人：刘玉坤 教授
时 间：10月14日10:00-11:00
地 点：Zoom会议ID：87374339181，密码：458830
报告人简介：
Yufeng Liu is Professor in Statistics and Biostatistics at University of North Carolina at Chapel Hill. His research interests include statistical machine learning, precision medicine， high dimensional data analysis, and bioinformatics. He is an elected fellow at American Statistical Association (ASA) and Institute of Mathematical Statistics (IMS).
报告内容简介：
Learning an optimal Individualized Treatment Rule (ITR) is a very important problem in precision medicine. In this talk, we consider the challenge when the number of treatment arms is large, and some groups of treatments in the large treatment space may work similarly for the patients. Motivated by the recent development of supervised clustering, we propose a novel adaptive fusion-based method to cluster the treatments with similar treatment effects together and estimate the optimal ITR simultaneously through a single convex optimization. We establish the theoretical guarantee of recovering the underlying true clustering structure of the treatments for our method. Finally, the superior performance of our method will be demonstrated via both simulations and a real data application on cancer treatment.
This is joint work with Haixu Ma and Donglin Zeng at UNC-Chapel Hill.