Skip to content. | Skip to navigation

Personal tools


You are here: Home / Coming Up / UMD SPH Dept of Epidemiology and Biostatistics Seminar

UMD SPH Dept of Epidemiology and Biostatistics Seminar

EPIB Research Seminar featuring Dr. Lu Wang, Associate Professor of Biostatistics at University of Michigan School of Public Health
When Mar 28, 2019
from 10:30 AM to 11:30 AM
Where 2242H SPH Building
Add event to calendar vCal

About the Speaker

Dr. Lu Wang is an Associate Professor of Biostatistics at the University of Michigan, Ann Arbor. She received her Ph.D. in Biostatistics from Harvard University in 2008 and joined the Michigan faculty in the same year. She has been an Associate Editor (AE) of Biometrics from 2013 to 2017, and is currently an AE of Journal of American Statistical Association. Professor Wang's research focuses on statistical methods for evaluating dynamic treatment regimes, personalized health care, missing data analysis, and longitudinal data analysis. SEMINAR

About the Presentation

In this talk, we present recent advances and statistical developments for evaluating Dynamic Treatment Regimes (DTR), which allow the treatment to be dynamically tailored according to evolving subject-level data. Identification of an optimal DTR is a key component for precision medicine and personalized health care. We will first introduce a dynamic statistical learning method, adaptive contrast weighted learning (ACWL), which combines doubly robust semiparametric regression estimators with flexible machine learning methods. We will further develop a tree-based reinforcement learning (TRL) method, which directly estimates optimal DTRs in a multi-stage multi-treatment setting. At each stage, T-RL builds an unsupervised decision tree that maintains the nature of batchmode reinforcement learning, and handles the optimization problem with multiple treatment comparisons directly through the purity measure constructed with augmented inverse probability weighted estimators. By combining robust semiparametric regression with flexible tree-based learning, T-RL is robust, efficient and easy to interpret. However, ACWL seems more robust against tree-type misspecification than T-RL when the true optimal DTR is non-tree-type.

Filed under:
« July 2019 »