Difference in proportions is frequently used to measure treatment effect for binary outcomes in randomized clinical trials. The estimation of difference in proportions can be assisted by adjusting for prognostic baseline covariates to enhance precision and bolster statistical power. Standardization or G-computation is a widely used method for covariate adjustment in estimating unconditional difference in proportions, because of its robustness to model misspecification. Various inference methods have been proposed to quantify the uncertainty and confidence intervals based on large-sample theories. However, their performances under small sample sizes and model misspecification have not been comprehensively evaluated. We propose an alternative approach to estimate the unconditional variance of the standardization estimator based on the robust sandwich estimator to further enhance the finite sample performance. Extensive simulations are provided to demonstrate the performances of the proposed method, spanning a wide range of sample sizes, randomization ratios, and model misspecification. We apply the proposed method in a real data example to illustrate the practical utility.

翻译：在随机临床试验中，比例差常用于测量二分类结局的治疗效应。通过调整预后基线协变量可辅助比例差的估计，从而提高精度并增强统计功效。标准化或G计算是估计无条件比例差时广泛使用的协变量调整方法，因其对模型误设具有稳健性。已有多种基于大样本理论的推断方法用于量化不确定性和置信区间，但这些方法在小样本量和模型误设下的表现尚未被全面评估。我们提出一种替代方法，基于稳健三明治估计量来估计标准化估计量的无条件方差，以进一步改善有限样本表现。通过涵盖广泛样本量、随机化分配比例和模型误设情形的模拟研究，验证了所提方法的性能。最后，我们通过实际数据示例说明该方法的实用价值。