Report title: Multi-category individualized treatment regime using outcome weighted learning
Speaker: Associate Professor Yair Goldberg Technion University, Israel
Reporting time: 14:20-15:10, January 11, 2021
Report location: Zoom meeting (Zoom meeting id: 770 311 8512, password: 378548)
School contact: Wang Peijie email@example.com
Report summary: 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.
Brief introduction of the speaker:
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.