时 间：2024年7月29日 9:30 - 10:30
地 点：普陀校区理科大楼A1514
报告人：Annie Qu University of California, Irvine Professor
主持人：周勇华东师范大学教授
摘 要：
Recent advances in dynamic treatment regimes (DTRs) provide powerful optimal treatment searching algorithms, which are tailored to individuals’ specific needs and able to maximize their expected clinical benefits. However, existing algorithms could suffer from insufficient sample size under optimal treatments, especially for chronic diseases involving long stages of decision-making. To address these challenges, we propose a novel individualized learning method which estimates the DTR with a focus on prioritizing alignment between the observed treatment trajectory and the one obtained by the optimal regime across decision stages. By relaxing the restriction that the observed trajectory must be fully aligned with the optimal treatments, our approach substantially improves the sample efficiency and stability of inverse probability weighted based methods. In particular, the proposed learning scheme builds a more general framework which includes the popular outcome weighted learning framework as a special case of ours. Moreover, we introduce the notion of stage importance scores along with an attention mechanism to explicitly account for heterogeneity among decision stages. We establish the theoretical properties of the proposed approach, including the Fisher consistency and finite-sample performance bound. Empirically, we evaluate the proposed method in extensive simulated environments and a real case studyfor COVID-19 pandemic.
This paper received several student paper awards (given to Hanwen Ye) and two awards are the best paper awards: ENAR Van Ryzin award and Biometrics David Byar Early Career Award.
报告人简介：
Annie Qu is Chancellor’s Professor, Department of Statistics, University of California, Irvine. She received her Ph.D. in Statistics from the Pennsylvania State University in 1998. Qu’s research focuses on solving fundamental issues regarding structured and unstructured large-scale data and developing cutting-edge statistical methods and theory in machine learning and algorithms for personalized medicine, text mining, recommender systems, medical imaging data, and network data analyses for complex heterogeneous data. The newly developed methods can extract essential and relevant information from large volumes of intensively collected data, such as mobile health data. Her research impacts many fields, including biomedical studies, genomic research, public health research, social and political sciences. Before joining UC Irvine, Dr. Qu was a Data Science Founder Professor of Statistics and the Director of the Illinois Statistics Office at the University of Illinois at Urbana-Champaign. She was awarded the Brad and Karen Smith Professorial Scholar by the College of LAS at UIUC and was a recipient of the NSF Career award from 2004 to 2009. She is a Fellow of the Institute of Mathematical Statistics (IMS), the American Statistical Association, and the American Association for the Advancement of Science. She is also a recipient of IMS Medallion Award and Lecturer in 2024. She serves as Journal of the American Statistical Association Theory and Methods Co-Editor from 2023 to 2025 and as IMS Program Secretary from 2021 to 2027.