Suzuki Etsuji, Mitsuhashi Toshiharu, Tsuda Toshihide, Yamamoto Eiji
Department of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Japan.
Center for Innovative Clinical Medicine, Okayama University Hospital, Okayama University, Okayama, Japan.
J Epidemiol. 2017 Feb;27(2):49-55. doi: 10.1016/j.je.2016.09.003. Epub 2016 Nov 18.
Confounding is a major concern in epidemiology. Despite its significance, the different notions of confounding have not been fully appreciated in the literature, leading to confusion of causal concepts in epidemiology. In this article, we aim to highlight the importance of differentiating between the subtly different notions of confounding from the perspective of counterfactual reasoning. By using a simple example, we illustrate the significance of considering the distribution of response types to distinguish causation from association, highlighting that confounding depends not only on the population chosen as the target of inference, but also on the notions of confounding in distribution and confounding in measure. This point has been relatively underappreciated, partly because some literature on the concept of confounding has only used the exposed and unexposed groups as the target populations, while it would be helpful to use the total population as the target population. Moreover, to clarify a further distinction between confounding "in expectation" and "realized" confounding, we illustrate the usefulness of examining the distribution of exposure status in the target population. To grasp the explicit distinction between confounding in expectation and realized confounding, we need to understand the mechanism that generates exposure events, not the product of that mechanism. Finally, we graphically illustrate this point, highlighting the usefulness of directed acyclic graphs in examining the presence of confounding in distribution, in the notion of confounding in expectation.
混杂是流行病学中的一个主要问题。尽管其具有重要意义，但文献中对不同的混杂概念并未得到充分认识，导致流行病学中因果概念的混淆。在本文中，我们旨在从反事实推理的角度强调区分细微不同的混杂概念的重要性。通过一个简单的例子，我们说明了考虑反应类型分布以区分因果关系与关联的重要性，强调混杂不仅取决于作为推断目标选择的人群，还取决于分布混杂和测量混杂的概念。这一点相对未得到充分认识，部分原因是一些关于混杂概念的文献仅将暴露组和未暴露组作为目标人群，而将总人口作为目标人群会有所帮助。此外，为了阐明“预期”混杂和“实际”混杂之间的进一步区别，我们说明了检查目标人群中暴露状态分布的有用性。要掌握预期混杂和实际混杂之间的明确区别，我们需要了解产生暴露事件的机制，而不是该机制的产物。最后，我们用图形说明了这一点，强调了有向无环图在检查预期混杂概念中分布混杂存在情况方面的有用性。
