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：为提高二元结果随机临床试验中无条件处理效应的估计精度和假设检验效能，研究人员与监管机构推荐使用g-computation作为可靠的协变量调整方法。然而，g-computation的实际应用因缺乏适用于不同目标无条件处理效应的显式稳健方差公式而受到制约。为填补这一空白，我们提出了g-computation估计量的显式稳健方差估计方法，并通过模拟研究验证了这些方差估计量在实践中具有可靠的应用价值。