Evidence synthesis for constructing directed acyclic graphs (ESC-DAGs): a novel and systematic method for building directed acyclic graphs
Evidence synthesis for constructing directed acyclic graphs (ESC-DAGs): a novel and systematic method for building directed acyclic graphs
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DOI：
10.1093/ije/dyz150
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发表时间：
2020-02-01
影响因子：
7.7
通讯作者：
Lewsey, James D.
中科院分区：
文献类型：
--
作者：
Ferguson, Karl D.;McCann, Mark;Lewsey, James D.
背景：有向无环图（DAG）是流行病学分析中确定适当调整策略的常用工具。然而，缺乏关于如何建立它们的指导是有问题的。作为一种解决方案，我们建议使用证据合成策略和因果推理原则相结合，将DAG建设工作的研究项目的审查阶段。我们证明了这一点，通过引入一种新的协议：“证据合成构建有向无环图”（ESC-DAGS“。方法：ESC-DAGS操作的实证研究确定的文献检索，理想的是一种新的系统性综述或系统性综述的审查。它涉及三个关键阶段：（i）每个研究的结论被“映射”到一个DAG中;（ii）这些DAG中的因果结构使用几个因果推理原则进行系统评估，并相应地进行校正;（iii）所得到的DAG然后被合成为一个或多个“综合DAG”。这篇示范文章教学适用于ESC-DAGs的文学对后代酒精使用在adolescence.Conclusions的父母的影响：ESC-DAGs是一个实用的，系统的和透明的方法，从背景知识发展DAGs。然后，这些DAG可以指导原始数据分析和基于DAG的敏感性分析。ESC-DAG具有模块化设计，允许有经验的DAG用户的研究人员使用和改进该方法。它也适用于对DAG或证据合成经验有限的研究人员。
Background: Directed acyclic graphs (DAGs) are popular tools for identifying appropriate adjustment strategies for epidemiological analysis. However, a lack of direction on how to build them is problematic. As a solution, we propose using a combination of evidence synthesis strategies and causal inference principles to integrate the DAG-building exercise within the review stages of research projects. We demonstrate this idea by introducing a novel protocol: 'Evidence Synthesis for Constructing Directed Acyclic Graphs' (ESC-DAGs)'.Methods: ESC-DAGs operates on empirical studies identified by a literature search, ideally a novel systematic review or review of systematic reviews. It involves three key stages: (i) the conclusions of each study are 'mapped' into a DAG; (ii) the causal structures in these DAGs are systematically assessed using several causal inference principles and are corrected accordingly; (iii) the resulting DAGs are then synthesised into one or more 'integrated DAGs'. This demonstration article didactically applies ESC-DAGs to the literature on parental influences on offspring alcohol use during adolescence.Conclusions: ESC-DAGs is a practical, systematic and transparent approach for developing DAGs from background knowledge. These DAGs can then direct primary data analysis and DAG-based sensitivity analysis. ESC-DAGs has a modular design to allow researchers who are experienced DAG users to both use and improve upon the approach. It is also accessible to researchers with limited experience of DAGs or evidence synthesis.