报告题目：Sequential Covariate-adjusted Randomization via Hierarchically Minimizing Mahalanobis Distance and Marginal Imbalance
报告人：李扬 教授（院长）
单位：中国人民大学统计学院
时间：2025年09月16日 15:00-18:30
地点：线上腾讯会议：198 469 434
摘要：
In comparative studies, covariate balance and sequential allocation schemes have attracted growing academic interest. Although many theoretically justified adaptive randomization methods achieve the covariate balance, they often allocate patients in pairs or groups. To better meet the practical requirements where the clinicians cannot wait for other participants to assign the current patient for some economic or ethical reasons, we propose a method that randomizes patients individually and sequentially. The proposed method conceptually separates the covariate imbalance, measured by the newly proposed modified Mahalanobis distance, and the marginal imbalance, that is the sample size difference between the 2 groups, and it minimizes them with an explicit priority order. Compared with the existing sequential randomization methods, the proposed method achieves the best possible covariate balance while maintaining the marginal balance directly, offering us more control of the randomization process. We demonstrate the superior performance of the proposed method through a wide range of simulation studies and real data analysis, and also establish theoretical guarantees for the proposed method in terms of both the convergence of the imbalance measure and the subsequent treatment effect estimation.
报告人简介：
李扬，中国人民大学吴玉章特聘教授、博士生导师，学校交叉科学学术委员会副主任，统计学院院长，入选国家级青年人才项目；担任国际统计学会Elected Member、中国商业统计学会副会长、中国统计学会常务理事、中国现场统计研究会常务理事等；主要从事模型选择与不确定性评价、复杂调查设计与分析、潜变量建模、试验设计与推断等领域研究，在JASA、AOAS、Biometrics等期刊发表论文九十余篇，承担国家自然科学基金、教育部重大项目、全国统计科学研究重大项目等。
