報告題目: Multi-category individualized treatment regime using outcome weighted learning
報告人： Yair Goldberg 副教授 以色列Technion大學
報告時間：2021年1月11日 14:20-15:10
報告地點：Zoom 會議 (Zoom 會議id: 770 311 8512, 密碼: 378548)
校内聯系人：王培潔 wangpeijie@jlu.edu.cn
報告摘要：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.
報告人簡介：
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.
