  In observational studies with time-to-event outcomes, the g-formula can be
used to estimate a treatment effect in the presence of confounding factors.
However, the asymptotic distribution of the corresponding stochastic process is
complicated and thus not suitable for deriving confidence intervals or
time-simultaneous confidence bands for the average treatment effect. A common
remedy are resampling-based approximations, with Efron's nonparametric
bootstrap being the standard tool in practice. We investigate the large sample
properties of three different resampling approaches and prove their asymptotic
validity in a setting with time-to-event data subject to competing risks.
