报告题目: Multi-category individualized treatment regime using outcome weighted learning
报告人： Yair Goldberg 副教授 以色列Technion大学
报告地点：Zoom 会议 (Zoom 会议id: 770 311 8512, 密码: 378548)
报告摘要：Individualized treatment regimes (ITRs) aim to recommend treatments based on patient-specific characteristics in order to maximize the expected clinical outcome. Outcome weighted learning approaches have been proposed for this optimization problem with a primary focus on the binary treatment case. Many require assumptions of the outcome value or the randomization mechanism. In this talk, I will present a general framework for multi-category ITRs using generic surrogate risk. The proposed method accommodates the situations when the outcome takes a negative value and/or when the propensity score is unknown. Theoretical results, simulation results, and application to data from a clinical trial will be presented.
This is joint work with Xinyang Huang and Jin Xu.
I obtained my Ph.D. in Statistics from the Hebrew University of Jerusalem in 2009. From 2009 to 2011, I conducted postdoctoral studies at the Department of Biostatistics at UNC-Chapel Hill. From 2011 to 2018, I was a faculty at the University of Haifa. Since 2018, I am a faculty member at the Faculty of Industrial Engineering and Management in the Technion. My research interests include statistical theory and machine learning mostly in the biostatistics context. I currently work on research topics in both of these fields, and at the interface between them.