Balancing Unobserved Covariates With Covariate-Adaptive Randomized Experiments

• 成果类型: Article
• 署名作者: Liu, Yang; Hu, Feifang
• 署名单位: George Washington University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2020.1825450
• 发表日期: 2022
• 页码: 875-886
• 关键词: clinical-trials; DESIGN

摘要

Balancing important covariates is often critical in clinical trials and causal inference. Stratified permuted block (STR-PB) and covariate-adaptive randomization (CAR) procedures are widely used to balance observed covariates in practice. The balance properties of these procedures with respect to the observed covariates have been well studied. However, it has been questioned whether these methods will also yield a good balance for the unobserved covariates. In this article, we develop a general framework for the analysis of the unobserved covariates imbalance. These results are applicable to develop and compare the balance properties of complete randomization (CR), STR-PB, and CAR procedures with respect to the unobserved covariates. To quantify the improvement obtained by using STR-PB and CAR procedures rather than CR, we introduce the percentage reduction in variance of the unobserved covariates imbalance and compare these quantities. Our results demonstrate the benefits of using CAR or STR-PB (when the number of strata is small relative to the sample size) in terms of balancing unobserved covariates. These results also pave the way for future research into the effect of unobserved covariates in covariate-adaptive randomized experiments in clinical trials, as well as many other applications. for this article are available online.