Zhang Zhiwei
Biostatistics Innovation Group, Gilead Sciences Inc, Foster City, CA, USA.
Stat Methods Med Res. 2025 Apr;34(4):829-844. doi: 10.1177/09622802251324764. Epub 2025 Mar 20.
The statistical efficiency of randomized clinical trials can be improved by incorporating information from baseline covariates (i.e. pre-treatment patient characteristics). This can be done in the design stage using stratified (permutated block) randomization or in the analysis stage through covariate adjustment. This article makes a connection between covariate adjustment and stratified randomization in a general framework where all regular, asymptotically linear estimators are identified as augmented estimators. From a geometric perspective, covariate adjustment can be viewed as an attempt to approximate the optimal augmentation function, and stratified randomization improves a given approximation by moving it closer to the optimal augmentation function. The efficiency benefit of stratified randomization is asymptotically equivalent to attaching an optimal augmentation term based on the stratification factor. In designing a trial with stratified randomization, it is not essential to include all important covariates in the stratification, because their prognostic information can be incorporated through covariate adjustment. Under stratified randomization, adjusting for the stratification factor only in data analysis is not expected to improve efficiency, and the key to efficient estimation is incorporating prognostic information from all important covariates. These observations are confirmed in a simulation study and illustrated using real clinical trial data.
通过纳入基线协变量（即治疗前患者特征）的信息，可以提高随机临床试验的统计效率。这可以在设计阶段使用分层（排列区组）随机化来完成，或者在分析阶段通过协变量调整来实现。本文在一个通用框架中建立了协变量调整与分层随机化之间的联系，在该框架中，所有正则、渐近线性估计量都被识别为增强估计量。从几何角度来看，协变量调整可以被视为一种近似最优增强函数的尝试，而分层随机化通过使其更接近最优增强函数来改进给定的近似。分层随机化的效率优势渐近等同于基于分层因素附加一个最优增强项。在设计分层随机化试验时，不必在分层中纳入所有重要协变量，因为它们的预后信息可以通过协变量调整来纳入。在分层随机化下，仅在数据分析中对分层因素进行调整预计不会提高效率，有效估计的关键是纳入所有重要协变量的预后信息。这些观察结果在模拟研究中得到了证实，并使用真实的临床试验数据进行了说明。
