[Lecture] Lan Wang: Transformation-Invariant Learning of Optimal Individualized Decision Rules with Time-to-Event Outcomes
Time: 9:00-10:30am, Apr. 21th, 2022, Thursday
Venue: Zoom ID：84307291432, Password:382243
Spreker: Lan Wang, Professor, Department of Management Science, University of Miami
Abstract:
In many important applications of precision medicine, the outcome of interest is time to an event (e.g., death, relapse of disease) and the primary goal is to identify the optimal individualized decision rule (IDR) to prolong survival time. Existing work in this area has been mostly focused on estimating the optimal IDR to maximize the restricted mean survival time in the population. We propose a new robust framework for estimating an optimal static or dynamic IDR with time-to-event outcomes based on an easy-to-interpret quantile criterion. The new method does not need to specify an outcome regression model and is robust for heavy-tailed distribution. The estimation problem corresponds to a nonregular M-estimation problem with both finite and infinite-dimensional nuisance parameters. Employing advanced empirical process techniques, we establish the statistical theory of the estimated parameter indexing the optimal IDR. Furthermore, we prove a novel result that the proposed approach can consistently estimate the optimal value function under mild conditions even when the optimal IDR is non-unique, which happens in the challenging setting of exceptional laws. We also propose a smoothed resampling procedure for inference. The proposed methods are implemented in the R-package QTOCen. We demonstrate the performance of the proposed new methods via extensive Monte Carlo studies and a real data application.
Speaker’s Bio:
Dr. Lan Wang is a tenured Professor in the Department of Management Science at the Miami Herbert Business School of the University of Miami, with a secondary appointment as Professor of Public Health Sciences at the Miller School of Medicine, University of Miami. She currently serves as the Co-Editor for Annals of Statistics (2022-2024), jointly with Professor Enno Mammen. Dr. Wang's research covers several interrelated areas: high-dimensional statistical learning, quantile regression, optimal personalized decision recommendation, and survival analysis. She is also interested in interdisciplinary collaboration, driven by applications in healthcare, business, economics, and other domains. Dr. 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 was the associate editor for several leading statistical journals: Journal of the American Statistical Associations, Annals of Statistics, Journal of the Royal Statistical Society, and Biometrics.
