Keyes Katherine M, Tracy Melissa, Mooney Stephen J, Shev Aaron, Cerdá Magdalena
Am J Epidemiol. 2017 Jul 15;186(2):146-148. doi: 10.1093/aje/kwx090.
Agent-based models (ABMs) have grown in popularity in epidemiologic applications, but the assumptions necessary for valid inference have only partially been articulated. In this issue, Murray et al. (Am J Epidemiol. 2017;186(2):131-142) provided a much-needed analysis of the consequence of some of these assumptions, comparing analysis using an ABM to a similar analysis using the parametric g-formula. In particular, their work focused on the biases that can arise in ABMs that use parameters drawn from distinct populations whose causal structures and baseline outcome risks differ. This demonstration of the quantitative issues that arise in transporting effects between populations has implications not only for ABMs but for all epidemiologic applications, because making use of epidemiologic results requires application beyond a study sample. Broadly, because health arises within complex, dynamic, and hierarchical systems, many research questions cannot be answered statistically without strong assumptions. It will require every tool in our store of methods to properly understand population dynamics if we wish to build an evidence base that is adequate for action. Murray et al.'s results provide insight into these assumptions that epidemiologists can use when selecting a modeling approach.
基于主体的模型（ABMs）在流行病学应用中越来越受欢迎，但有效推断所需的假设仅得到了部分阐述。在本期中，默里等人（《美国流行病学杂志》。2017年；186(2):131 - 142）对其中一些假设的后果进行了急需的分析，将使用ABM的分析与使用参数化g公式的类似分析进行了比较。特别是，他们的工作重点关注了在使用从因果结构和基线结局风险不同的不同人群中得出的参数的ABM中可能出现的偏差。这种对人群间效应传递中出现的定量问题的论证不仅对ABM有影响，对所有流行病学应用也有影响，因为利用流行病学结果需要超出研究样本进行应用。广泛地说，由于健康产生于复杂、动态和分层的系统中，如果没有强有力的假设，许多研究问题无法通过统计学方法回答。如果我们希望建立一个足以指导行动的证据基础，就需要运用我们所有的方法工具来正确理解人群动态。默里等人的结果为流行病学家在选择建模方法时可以使用的这些假设提供了见解。
