Toward Better Practice of Covariate Adjustment in Analyzing Randomized Clinical Trials

TL;DR

This paper discusses improved practices for covariate adjustment in randomized clinical trials, emphasizing efficiency, applicability, and robustness of model-assisted methods under various randomization schemes.

Contribution

It introduces a comprehensive asymptotic theory for covariate adjustment, ensuring efficiency gains, broad applicability, and robust variance estimation in clinical trial analysis.

Findings

Model-assisted methods often improve efficiency without harm.

The proposed approach is applicable to all common randomization schemes.

Variance estimation is robust to model misspecification.

Abstract

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 model-assisted inference is applied to adjust for covariates under simple or covariate-adaptive randomized trials: (1) guaranteed efficiency gain: a model-assisted method should often gain but never hurt efficiency; (2) wide applicability: a valid procedure should be applicable, and preferably universally applicable, to all commonly used randomization schemes; (3) 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 including all covariates utilized 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.