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.
翻译:在随机临床试验中,监管机构大力鼓励在设计和分析阶段对基线共变数进行调整,在设计和分析阶段都大力鼓励对基准共变数进行调整。最近的趋势是采用模型辅助办法,共同变数调整,以获得可信度和效率,同时生成无现效的有效推论,即使模型不正确。在本条中,我们提出三个考虑因素,以便在应用模型辅助推论,在简单或共变调整随机试验中对共变数进行调整时,采取更好的做法:(1) 保证效率增益:模型辅助方法应经常提高但绝不会损害效率;(2) 广泛适用:对所有常用随机化计划适用而且最好普遍适用一个有效的程序;(3) 稳健的标准错误:差异估计应稳健有力,以模拟误差和异差度。为了实现这些,我们建议在分析混合共变工作模式(包括随机化中使用的所有共变数)的情况下,采用模型辅助估计器进行更好的调整。我们的结论基于一种无现的理论,该理论可以清晰地说明共变随机调整和倒退调整和回归性调整改变统计效率的方法;(3) 强标准:差异估计应强强,我们理论更普遍地研究现有武器效率的任意性比重。