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Iterated conditional expectation (ICE) g-computation is an estimationapproach for addressing time-varying confounding for both longitudinal andtime-to-event data. Unlike other g-computation implementations, ICE avoids theneed to specify models for each time-varying covariate. For varianceestimation, previous work has suggested the bootstrap. However, bootstrappingcan 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 estimatedusing the empirical sandwich variance estimator. Performance of the varianceestimator was evaluated empirically with a simulation study. The proposedapproach is also demonstrated with an illustrative example on the effect ofcigarette 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 thebootstrap for the applied example, the sandwich estimator was substantiallyfaster, even when bootstraps were run in parallel. The empirical sandwichvariance estimator is a viable option for variance estimation with ICEg-computation.

Empirical sandwich variance estimator for iterated conditional expectation g-computation