To improve precision of estimation and power of testing hypothesis for an unconditional treatment effect in randomized clinical trials with binary outcomes, researchers and regulatory agencies recommend using g-computation as a reliable method of covariate adjustment. However, the practical application of g-computation is hindered by the lack of an explicit robust variance formula that can be used for different unconditional treatment effects of interest. To fill this gap, we provide explicit and robust variance estimators for g-computation estimators and demonstrate through simulations that the variance estimators can be reliably applied in practice.

翻译：为提升二元结局随机临床试验中无条件处理效应的估计精度与假设检验效能，研究者及监管机构推荐使用g-computation作为可靠的协变量调整方法。然而，由于缺乏适用于不同感兴趣无条件处理效应的显式鲁棒方差公式，g-computation的实际应用受到阻碍。为填补这一空白，我们提供了g-computation估计量的显式鲁棒方差估计器，并通过模拟研究证明该方差估计器在实际应用中具有可靠性。