Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations.
Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations.
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使用定向的无环图（DAG）来识别应用健康研究中的混杂因素：审查和建议。
DOI：
10.1093/ije/dyaa213
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发表时间：
2021-05-17
影响因子：
7.7
通讯作者：
Ellison GTH
中科院分区：
文献类型：
--
作者：
Tennant PWG;Murray EJ;Arnold KF;Berrie L;Fox MP;Gadd SC;Harrison WJ;Keeble C;Ranker LR;Textor J;Tomova GD;Gilthorpe MS;Ellison GTH
有向无环图(DAG)是一种越来越流行的方法，用于识别在估计因果效应时需要条件作用的混杂变量。这篇综述考察了DAG在应用健康研究中的使用，以提供建议，以提高其在未来研究中的透明度和实用性。从Scope us、Web of Science、Medline和Embase中确定了1999-2017年间发表的提及“有向无环图”(或类似)或引用DAGitty的原始健康研究文章。摘录了关于以下报告的数据：统计数据、数据采集和调整集，以及每篇文章中最大的数据采集数据的特点。共确定了234篇使用DAG进行报告的文章。五分之一(n = 48，21%)报告了他们的目标估计(S)和一半(n = 115，48%)报告了他们的DAG所暗示的调整集(S)。三分之二的文章(n = 144，62%)至少提供了一个DAG。DAG大小不一，但平均为12个节点[四分位数范围：9-16，范围：3-28]和29条弧线(IQR：19-42，范围：3-99)。中位饱和度(即总可能弧线的百分比)为46%(IQR：31-67，范围：12-100)。37%(n = 53)的DAG包含未观察到的变量，17%(n = 25)包含‘超级节点’(即包含一个以上变量的节点)，34%(n = 49)直观地排列，以使组成弧沿同一方向(例如，从上到下)流动。在应用卫生研究中，DAG的使用和报告有很大的差异。虽然这在一定程度上反映了它们的灵活性，但也突显了一些潜在的改进领域。因此，这项审查提出了若干建议，以改进未来研究中报告和使用DAG的情况。
Directed acyclic graphs (DAGs) are an increasingly popular approach for identifying confounding variables that require conditioning when estimating causal effects. This review examined the use of DAGs in applied health research to inform recommendations for improving their transparency and utility in future research. Original health research articles published during 1999–2017 mentioning ‘directed acyclic graphs’ (or similar) or citing DAGitty were identified from Scopus, Web of Science, Medline and Embase. Data were extracted on the reporting of: estimands, DAGs and adjustment sets, alongside the characteristics of each article’s largest DAG. A total of 234 articles were identified that reported using DAGs. A fifth (n = 48, 21%) reported their target estimand(s) and half (n = 115, 48%) reported the adjustment set(s) implied by their DAG(s). Two-thirds of the articles (n = 144, 62%) made at least one DAG available. DAGs varied in size but averaged 12 nodes [interquartile range (IQR): 9–16, range: 3–28] and 29 arcs (IQR: 19–42, range: 3–99). The median saturation (i.e. percentage of total possible arcs) was 46% (IQR: 31–67, range: 12–100). 37% (n = 53) of the DAGs included unobserved variables, 17% (n = 25) included ‘super-nodes’ (i.e. nodes containing more than one variable) and 34% (n = 49) were visually arranged so that the constituent arcs flowed in the same direction (e.g. top-to-bottom). There is substantial variation in the use and reporting of DAGs in applied health research. Although this partly reflects their flexibility, it also highlights some potential areas for improvement. This review hence offers several recommendations to improve the reporting and use of DAGs in future research.
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