Using directed acyclic graphs to guide analyses of neighbourhood health effects: an introduction
Using directed acyclic graphs to guide analyses of neighbourhood health effects: an introduction
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DOI：
10.1136/jech.2007.067371
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发表时间：
2008-09-01
影响因子：
6.3
通讯作者：
Roux, A. V. Diez
中科院分区：
文献类型：
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作者：
Fleischer, N. L.;Roux, A. V. Diez
背景资料：有向无环图（DAG）是流行病学研究中的一种有用的图形工具，可以帮助确定适当的分析策略，以及常用方法（如调节介质）的潜在非预期后果。在社会因素对health.Methods的因果影响的研究中，使用DAGs可以提供特别丰富的信息：作者考虑了四种具体情况，其中DAGs可能对邻里健康影响研究人员有用：（1）确定在估计邻里健康影响时需要调整的变量，（2）确定通过调节中介来估计“直接”影响的意外后果，（3）使用DAG来了解可能的来源和后果的选择偏差在邻里健康影响的研究，和（4）使用DAG来确定的后果调整变量受先前exposure.Conclusions影响：作者提出了简化的样本DAG为每种情况下，讨论的见解，可以收集到的DAG在每种情况下，这些分析方法的影响。
Background: Directed acyclic graphs, or DAGs, are a useful graphical tool in epidemiologic research that can help identify appropriate analytical strategies in addition to potential unintended consequences of commonly used methods such as conditioning on mediators. The use of DAGs can be particularly informative in the study of the causal effects of social factors on health.Methods: The authors consider four specific scenarios in which DAGs may be useful to neighbourhood health effects researchers: (1) identifying variables that need to be adjusted for in estimating neighbourhood health effects, (2) identifying the unintended consequences of estimating "direct'' effects by conditioning on a mediator, (3) using DAGs to understand possible sources and consequences of selection bias in neighbourhood health effects research, and (4) using DAGs to identify the consequences of adjustment for variables affected by prior exposure.Conclusions: The authors present simplified sample DAGs for each scenario and discuss the insights that can be gleaned from the DAGs in each case and the implications these have for analytical approaches.