  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.
