Toward Better Practice of Covariate Adjustment in Analyzing Randomized Clinical Trials

• 成果类型: Article
• 署名作者: Ye, Ting; Shao, Jun; Yi, Yanyao; Zhao, Qingyuan
• 署名单位: University of Washington; University of Washington Seattle; East China Normal University; University of Wisconsin System; University of Wisconsin Madison; Eli Lilly; University of Cambridge
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2022.2049278
• 发表日期: 2023
• 页码: 2370-2382
• 关键词: biased coin randomization; regression adjustments; adaptive randomization; binary outcomes; estimators; allocation; inference

摘要

In randomized clinical trials, adjustments for baseline covariates at both design and analysis stages are highly encouraged by regulatory agencies. A recent trend is to use a model-assisted approach for covariate adjustment to gain credibility and efficiency while producing asymptotically valid inference even when the model is incorrect. In this article we present three considerations for better practice when modelassisted inference is applied to adjust for covariates under simple or covariate-adaptive randomized trials: (a) guaranteed efficiency gain: a model-assisted method should often gain but never hurt efficiency; (b) wide applicability: a valid procedure should be applicable, and preferably universally applicable, to all commonly used randomization schemes; (c) robust standard error: variance estimation should be robust to model misspecification and heteroscedasticity. To achieve these, we recommend a model-assisted estimator under an analysis of heterogeneous covariance working model that includes all covariates used in randomization. Our conclusions are based on an asymptotic theory that provides a clear picture of how covariate-adaptive randomization and regression adjustment alter statistical efficiency. Our theory is more general than the existing ones in terms of studying arbitrary functions of response means (including linear contrasts, ratios, and odds ratios), multiple arms, guaranteed efficiency gain, optimality, and universal applicability. Supplementary materials for this article are available online.