G-formula is a popular approach for estimating treatment or exposure effects from longitudinal data that are subject to time-varying confounding. G-formula estimation is typically performed by Monte-Carlo simulation, with non-parametric bootstrapping used for inference. We show that G-formula can be implemented by exploiting existing methods for multiple imputation (MI) for synthetic data. This involves using an existing modified version of Rubin's variance estimator. In practice missing data is ubiquitous in longitudinal datasets. We show that such missing data can be readily accommodated as part of the MI procedure, and describe how MI software can be used to implement the approach. We explore its performance using a simulation study.

翻译：G-公式是一种常用的方法,用于估计从具有时间差异的纵向数据中产生的处理或接触影响。G-公式估计通常由Monte-Carlo模拟进行,使用非参数式的靴子进行推理。我们表明,G-公式可以通过利用现有的多种估算方法(MI)来实施,这涉及使用Rubin差异测算器的现有修改版本。实际上,缺失的数据在纵向数据集中是无处不在的。我们表明,这种缺失的数据可以很容易地作为MI程序的一部分被吸收,并描述如何使用MI软件实施这一方法。我们用模拟研究来探索其性能。