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

Robust Variance Estimation for Covariate-Adjusted Unconditional Treatment Effect in Randomized Clinical Trials with Binary Outcomes