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证据综合构建有向无环图(ESC-DAGs):一种构建有向无环图的新颖而系统的方法。

Evidence synthesis for constructing directed acyclic graphs (ESC-DAGs): a novel and systematic method for building directed acyclic graphs.

机构信息

MRC / CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK.

Mental Health and Wellbeing, University of Glasgow, Glasgow, UK.

出版信息

Int J Epidemiol. 2020 Feb 1;49(1):322-329. doi: 10.1093/ije/dyz150.

Abstract

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.

摘要

背景

有向无环图(DAG)是用于确定流行病学分析中适当调整策略的流行工具。但是,缺乏构建它们的指导是有问题的。作为一种解决方案,我们建议将证据综合策略和因果推理原则结合使用,将 DAG 构建练习纳入研究项目的审查阶段。我们通过引入一个新的方案来演示这个想法:“用于构建有向无环图的证据综合”(ESC-DAG)”。

方法

ESC-DAG 作用于通过文献搜索确定的实证研究,理想情况下是一项新的系统评价或系统评价综述。它涉及三个关键阶段:(i)每项研究的结论都“映射”到 DAG 中;(ii)使用几个因果推理原则系统地评估这些 DAG 中的因果结构,并进行相应的修正;(iii)然后将生成的 DAG 综合成一个或多个“综合 DAG”。本文通过教学应用 ESC-DAG 对关于父母对青少年期子女饮酒影响的文献进行了演示。

结论

ESC-DAG 是一种从背景知识中开发 DAG 的实用、系统和透明的方法。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 用户使用和改进该方法。它也适用于对 DAG 或证据综合经验有限的研究人员。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8f5/7124493/6eaa8fb567f7/dyz150f1.jpg

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