Journal
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Volume -, Issue -, Pages -Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/01621459.2022.2049278
Keywords
Analysis of covariance; Covariate-adaptive randomization; Efficiency; Heteroscedasticity; Model-assisted; Multiple treatment arms; Treatment-by-covariate interaction
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Funding
- National Natural Science Foundation of China
- U.S. National Science Foundation
- Isaact Newton Trust
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This article presents best practices for covariate adjustment in simple or covariate-adaptive randomized trials using a model-assisted approach. The recommendations include guaranteed efficiency gain, wide applicability, and robust standard error estimation.
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.
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