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 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.

翻译：迭代条件期望(ICE) g计算是一种用于处理纵向数据和时序事件数据中时变混杂变量的估计方法。与其他g计算实现不同，ICE避免了为每个时变协变量指定模型的需求。在方差估计方面，先前研究建议采用自助法（bootstrap）。然而，自助法计算强度高且对重抽样次数敏感。本文提出将ICE g计算表示为堆叠估计方程系统，因此可通过经验夹心方差估计量来估计ICE g计算估计量的方差。通过模拟研究对该方差估计量的性能进行了经验评估，并以吸烟对高血压患病率影响的示例论证了所提方法。模拟研究表明，经验夹心方差估计量能恰当地估计方差。在应用示例中比较夹心方差估计量与自助法的运行时间时，即使采用并行自助法，夹心估计量的计算速度仍显著更快。经验夹心方差估计量是ICE g计算中方差估计的可行选择。