McGrath Sean, Lin Victoria, Zhang Zilu, Petito Lucia C, Logan Roger W, Hernán Miguel A, Young Jessica G
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
These authors contributed equally.
Patterns (N Y). 2020 Jun 12;1(3). doi: 10.1016/j.patter.2020.100008. Epub 2020 May 18.
Researchers are often interested in estimating the causal effects of sustained treatment strategies, i.e., of (hypothetical) interventions involving time-varying treatments. When using observational data, estimating those effects requires adjustment for confounding. However, conventional regression methods cannot appropriately adjust for confounding in the presence of treatment-confounder feedback. In contrast, estimators derived from Robins's g-formula may correctly adjust for confounding even if treatment-confounder feedback exists. The package gfoRmula implements in R one such estimator: the parametric g-formula. This estimator can be used to estimate the effects of binary or continuous time-varying treatments as well as contrasts defined by static or dynamic, deterministic, or random interventions, as well as interventions that depend on the natural value of treatment. The package accommodates survival outcomes as well as binary or continuous outcomes measured at the end of follow-up. This paper describes the gfoRmula package, along with motivating background, features, and examples.
研究人员通常对估计持续治疗策略的因果效应感兴趣,即涉及随时间变化治疗的(假设的)干预措施的因果效应。在使用观察性数据时,估计这些效应需要对混杂因素进行调整。然而,在存在治疗-混杂因素反馈的情况下,传统回归方法无法适当地调整混杂因素。相比之下,源自罗宾斯g公式的估计器即使存在治疗-混杂因素反馈,也可能正确地调整混杂因素。gfoRmula软件包在R语言中实现了这样一种估计器:参数化g公式。该估计器可用于估计二元或连续随时间变化治疗的效应,以及由静态或动态、确定性或随机干预定义的对比,以及依赖于治疗自然值的干预。该软件包适用于生存结局以及随访结束时测量的二元或连续结局。本文介绍了gfoRmula软件包,以及激励背景、功能和示例。