Edwards Jessie K, Lesko Catherine R, Keil Alexander P
Am J Epidemiol. 2017 Jul 15;186(2):143-145. doi: 10.1093/aje/kwx089.
The g-formula and agent-based models (ABMs) are 2 approaches used to estimate causal effects. In the current issue of the Journal, Murray et al. (Am J Epidemiol. 2017;186(2):131-142) compare the performance of the g-formula and ABMs to estimate causal effects in 3 target populations. In their thoughtful paper, the authors outline several reasons that a causal effect estimated using an ABM may be biased when parameterized from at least 1 source external to the target population. The authors have addressed an important issue in epidemiology: Often causal effect estimates are needed to inform public health decisions in settings without complete data. Because public health decisions are urgent, epidemiologists are frequently called upon to estimate a causal effect from existing data in a separate population rather than perform new data collection activities. The assumptions needed to transport causal effects to a specific target population must be carefully stated and assessed, just as one would explicitly state and analyze the assumptions required to draw internally valid causal inference in a specific study sample. Considering external validity in important target populations increases the impact of epidemiologic studies.
g公式和基于主体的模型（ABM）是用于估计因果效应的两种方法。在本期《杂志》中，默里等人（《美国流行病学杂志》。2017年；186(2):131 - 142）比较了g公式和ABM在三个目标人群中估计因果效应的性能。在他们深思熟虑的论文中，作者概述了几个原因，即当从目标人群之外的至少一个来源进行参数化时，使用ABM估计的因果效应可能存在偏差。作者解决了流行病学中的一个重要问题：在没有完整数据的情况下，公共卫生决策通常需要因果效应估计来提供信息。由于公共卫生决策紧迫，流行病学家经常被要求从另一个单独人群的现有数据中估计因果效应，而不是开展新的数据收集活动。将因果效应推导至特定目标人群所需的假设必须仔细阐述和评估，就如同在特定研究样本中明确阐述和分析得出内部有效因果推断所需的假设一样。考虑重要目标人群中的外部有效性会增加流行病学研究的影响力。
