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

Karl D Ferguson, Mark McCann, Srinivasa Vittal Katikireddi, Hilary Thomson, Michael J Green, Daniel J Smith, James D Lewsey

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)322-329
Number of pages8
JournalInternational Journal of Epidemiology
Volume49
Issue number1
DOIs
Publication statusPublished - 1 Feb 2020

Keywords

  • Bias
  • Biomedical Research/methods
  • Causality
  • Confounding Factors, Epidemiologic
  • Data Interpretation, Statistical
  • Humans
  • Models, Statistical

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