  Iterated conditional expectation (ICE) g-computation is an estimation
approach for addressing time-varying confounding for both longitudinal and
time-to-event data. Unlike other g-computation implementations, ICE avoids the
need to specify models for each time-varying covariate. For variance
estimation, previous work has suggested the bootstrap. However, bootstrapping
can be computationally intense and sensitive to the number of resamples used.
Here, we present ICE g-computation as a set of stacked estimating equations.
Therefore, the variance for the ICE g-computation estimator can be consistently
estimated using the empirical sandwich variance estimator. Performance of the
variance estimator was evaluated empirically with a simulation study. The
proposed approach is also demonstrated with an illustrative example on the
effect of cigarette smoking on the prevalence of hypertension. In the
simulation study, the empirical sandwich variance estimator appropriately
estimated the variance. When comparing runtimes between the sandwich variance
estimator and the bootstrap for the applied example, the sandwich estimator was
substantially faster, even when bootstraps were run in parallel. The empirical
sandwich variance estimator is a viable option for variance estimation with ICE
g-computation.
