Department of Applied Mathematics, Computer Science and Statistics, Faculty of Sciences, Ghent University, Ghent, Belgium.
Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Am J Epidemiol. 2018 May 1;187(5):1079-1084. doi: 10.1093/aje/kwx347.
G-estimation is a flexible, semiparametric approach for estimating exposure effects in epidemiologic studies. It has several underappreciated advantages over other propensity score-based methods popular in epidemiology, which we review in this article. However, it is rarely used in practice, due to a lack of off-the-shelf software. To rectify this, we show a simple trick for obtaining G-estimators of causal risk ratios using existing generalized estimating equations software. We extend the procedure to more complex settings with time-varying confounders.
G 估计是一种灵活的半参数方法，用于估计流行病学研究中的暴露效应。它具有一些其他在流行病学中流行的基于倾向得分的方法所没有的优点，我们在本文中对此进行了回顾。然而，由于缺乏现成的软件，它在实践中很少被使用。为了解决这个问题，我们展示了一个简单的技巧，即在使用现有的广义估计方程软件来获得因果风险比的 G 估计。我们将该程序扩展到具有时变混杂因素的更复杂的设置。
