报告题目：Change surface regression for nonlinear subgroup identification

报告人：Li Jialiang 教授 （新加坡国立大学）

报告时间：2025年7月15日 （星期二）上午10：00-11：00

报告地点：数学科学学院111A

邀请人：王晓光 副教授   联系电话：84708351-8508

报告摘要：Pharmacogenomics stands as a pivotal driver toward personalized medicine, aiming to optimize drug efficacy while minimizing adverse effects by uncovering the impact of genetic variations on inter-individual outcome variability. Despite its promise, the intricate landscape of drug metabolism introduces complexity, where the correlation between drug response and genes can be shaped by numerous nongenetic factors, often exhibiting heterogeneity across diverse subpopulations. This challenge is particularly pronounced in datasets such as the International Warfarin Pharmacogenetic Consortium (IWPC), which encompasses diverse patient information from multiple nations. To capture the between-patient heterogeneity in dosing requirement, we formulate a novel change surface model as a model-based approach for multiple subgroup identification in complex datasets. A key feature of our approach is its ability to accommodate nonlinear subgroup divisions, providing a clearer understanding of dynamic drug-gene associations.

报告人简介：Prof. Jialiang Li obtained PhD in Statistics from University of Wisconsin Madison. Prof. Li is a distinguished researcher and best known for his contributions to statistical methodology in diagnostic medicine, statistical learning and personalized medicine. He has also collaborated extensively with medical researchers on numerous projects involving statistical analysis. Prof. Li is recognized by the statistical community as an Elected Member of International Statistical Institute (ISI), a Fellow of American Statistical Association (ASA), and a Fellow of Institute of Mathematical Statistics (IMS). He has made professional contributions by serving on the editorial board of Annals of Applied Statistics, Biometrics, Lifetime Data Analysis and other journals. He has served on Board of Director of International Chinese Statistical Association (ICSA), and Budget and Finance Committee of International Biometric Society (IBS).