Joint Applied Mathematics and Statistics Seminar Series: New Approaches for Inference on Optimal Treatment Regimes by Dr. Lan Wang
This is a past event.
Thursday, October 7 at 3:45pm to 4:45pmVirtual Event
Dr. Lan Wang is an elected Fellow of the American Statistical Association, an elected Fellow of the Institute of Mathematical Statistics, and an elected member of the International Statistical Institute. She is serving on the editorial boards of several leading statistical journals: Annals of Statistics, Journal of the American Statistical Associations, and Biometrics. She is the elected Co-Editor for Annals of Statistics (2022-2024). Before joining University of Miami, she was a Professor of Statistics at School of Statistics, University of Minnesota. She got her Ph.D. in Statistics from the Pennsylvania State University. She got her bachelor's degree in Applied Mathematics from Tsinghua University, China.
Dr. Wang will be speaking on "New Approaches for Inference on Optimal Treatment Regimes"
Abstract: Finding the optimal treatment regime (or a series of sequential treatment regimes) based on individual characteristics has important applications in precision medicine. We propose two new approaches to quantify uncertainty in optimal treatment regime estimation. First, we consider inference in the model-free setting, which does not require to specify an outcome regression model. Existing model-free estimators for optimal treatment regimes are usually not suitable for the purpose of inference, because they either have nonstandard asymptotic distributions or do not necessarily guarantee consistent estimation of the parameter indexing the Bayes rule due to the use of surrogate loss. We study a smoothed robust estimator that directly targets the parameter corresponding to the Bayes decision rule for optimal treatment regimes estimation. We verify that a resampling procedure provides asymptotically accurate inference for both the parameter indexing the optimal treatment regime and the optimal value function. Next, we consider the high-dimensional setting and propose a semiparametric model assisted approach for simultaneous inference. Simulations results and real data examples are used for illustration. (Joint work with Yunan Wu and Haoda Fu)
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Meeting ID: 951 4507 2763