Measurement error and information bias are ubiquitous in epidemiology, yet directed acyclic graphs (DAGs) are infrequently used to represent them, in contrast with confounding and selection bias. This represents a missed opportunity to leverage the full utility of DAGs to depict associations between the variables we actually analyse in practice: empirically measured variables, which are necessarily measured with error. In this article, we focus on applying causal diagrams to depict the data-generating mechanisms that give rise to the data we analyse, including measurement error. We begin by considering empirical data considerations using a general example, and then build up to a specific worked example from the clinical epidemiology of hearing health. Throughout, our goal is to highlight both the challenges and the benefits of using DAGs to depict measurement error. In addition to the application of DAGs to conceptual causal questions (which pertain to unmeasured constructs free from measurement error), which is common, we highlight the advantages associated with applying DAGs to also include empirically measured variables and-potentially-information bias. We also highlight the implications implied by this use of DAGs, particularly regarding the unblocked backdoor path causal structure. Ultimately, we seek to help increase the clarity with which epidemiologists can map traditional epidemiological concepts (such as information bias and confounding) onto causal graphical structures.

中文翻译：

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因果图中的测量误差和信息偏差：映射流行病学概念和图形结构。


测量误差和信息偏差在流行病学中无处不在，但与混杂和选择偏倚相反，有向无环图 （DAG） 很少用于表示它们。这代表着错失了利用 DAG 的全部效用来描述我们在实践中实际分析的变量之间的关联的机会：实证测量的变量，这些变量必然是用误差测量的。在本文中，我们重点介绍应用因果图来描述产生我们分析的数据的数据生成机制，包括测量误差。我们首先使用一个一般示例考虑经验数据考虑，然后从听力健康的临床流行病学中建立一个具体的工作示例。在整个过程中，我们的目标是强调使用 DAG 描述测量误差的挑战和好处。除了将 DAG 应用于概念因果问题（与没有测量误差的未测量结构有关）之外，这很常见，我们还强调了与应用 DAG 相关的优势，还包括经验测量的变量和潜在的信息偏差。我们还强调了使用 DAG 所隐含的影响，特别是关于未阻塞的后门路径因果结构。最终，我们寻求帮助提高流行病学家将传统流行病学概念（例如信息偏差和混杂因素）映射到因果图形结构上的清晰度。