Back to the program

Contributed Papers

Advances in Statistical Methods for Learning Individualized Treatments

Tue, Aug 6, 10:30 AM - 12:20 PM

Yizhe XuChair

Biometrics Section

6 Presentations

Bayesian outcome weighted learning for estimating sparse optimal individualized treatment rules

10:35 AM - 10:50 AM

Nikki Freeman (Duke University)

Co-authors: Sophia Yazzourh (Universite de Toulouse)

Causal Inference on Sequential Treatments via Tensor Completion

10:50 AM - 11:05 AM

Chenyin Gao (North Carolina State University)

Co-authors: Shu Yang (North Carolina State University, Department of Statistics), Anru Zhang (Duke University)

Estimating optimal individualized treatment regimes for survival outcomes in competing risk data

11:05 AM - 11:20 AM

Co-authors: Nikki Freeman (Duke University), Michael Kosorok (University of North Carolina at Chapel Hill)

Nonparametric Bayesian Q-learning for optimization of dynamic treatment regimes

11:20 AM - 11:35 AM

Indrabati Bhattacharya (Florida State University)

Co-authors: Ashkan Ertefaie (University of Rochester), Kevin Lynch (University of Pennsylvania), James McKay (University of Pennsylvania), Brent Johnson (University of Rochester-Medical Center)

Personalized Treatment Selection for Multivariate Ordinal Scale Outcomes and Multiple Treatments

11:35 AM - 11:50 AM

Chathura Siriwardhana (University of Hawaii)

Co-authors: Bakeerathan Gunaratnam (University of Louisville), K.B. Kulasekera (University of Louisville)

Tensor-Based Individualized Treatment Rules for Neuroimaging Applications

11:50 AM - 12:05 PM

Co-authors: Ting Li, Yuanying Chen, Yang Bai (Shanghai University of Finance and Ecnomics)

↑