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Data Analysis and Problem Solving Seminar
Data Analysis and Problem Solving Seminar
Covariate-adaptive design: An overview and recent advances
Covariate-adaptive design: An overview and recent advances
Organizer
Speaker
Wei Ma
Time
Friday, April 17, 2026 3:00 PM - 4:00 PM
Venue
A3-1-301
Online
Zoom 204 323 0165
(BIMSA)
Abstract
Covariate-adaptive designs are a class of experimental design methods that dynamically adjust treatment allocation probabilities to achieve balanced covariates across treatment groups. Because of their strengths in enhancing treatment group comparability, increasing the precision of treatment effect estimation, and producing more convincing experimental results, these designs are extensively employed in randomized controlled settings, including clinical trials, economic field experiments, and online A/B testing. This talk first provides a methodological review of various covariate-adaptive design approaches and then discusses a recent advancement in the field, which proposes a novel and unified framework for covariate-adaptive designs. The challenges and solutions in analyzing data collected from covariate-adaptive designs will also be addressed.
Speaker Intro
Wei Ma is an Associate Professor at the Institute of Statistics and Big Data, Renmin University of China. He earned his bachelor's degree from the Department of Mathematics, Zhejiang University, and his PhD from the Department of Statistics, the University of Virginia, USA. His research interests include adaptive design, clinical trial design and analysis, biostatistics, machine learning and artificial intelligence. He has published many academic papers in journals such as JASA, Biometrika, and Biometrics. He is an elected member of the International Statistical Institute and currently serves as an Associate Editor for journals including JASA and Statistica Sinica.