  The g-formula can be used to estimate the treatment effect while accounting
for confounding bias in observational studies. With regard to time-to-event
endpoints, possibly subject to competing risks, the construction of valid
pointwise confidence intervals and time-simultaneous confidence bands for the
causal risk difference is complicated, however. A convenient solution is to
approximate the asymptotic distribution of the corresponding stochastic process
by means of resampling approaches. In this paper, we consider three different
resampling methods, namely the classical nonparametric bootstrap, the influence
function equipped with a resampling approach as well as a martingale-based
bootstrap version. We set up a simulation study to compare the accuracy of the
different techniques, which reveals that the wild bootstrap should in general
be preferred if the sample size is moderate and sufficient data on the event of
interest have been accrued. For illustration, the three resampling methods are
applied to data on the long-term survival in patients with early-stage
Hodgkin's disease.
