Evidence map›Paper›PMID 31325312›Full record

ArticleInternational journal of epidemiology2020

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

Erratum issuedAbstract read
In one paragraph

Article in International journal of epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 91 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
91citing papers in PubMed, 5 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

91 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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  9. Beyond the patient: how providers perceive and experience non-medical barriers in gastrointestinal cancer care.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
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31 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Karl D FergusonMRC / CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK.
Mark McCannMRC / CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK.
Srinivasa Vittal KatikireddiMRC / CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK.
Hilary ThomsonMRC / CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK.
Michael J GreenMRC / CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK.
Daniel J SmithMental Health and Wellbeing, University of Glasgow, Glasgow, UK.
James D LewseyHealth Economics and Health Technology Assessment, University of Glasgow, Glasgow, UK.

Funding

Chief Scientist Office SCAF/15/02Chief Scientist Office SPHSU13Chief Scientist Office SPHSU14Chief Scientist Office SPHSU15Chief Scientist Office SPHSU17Chief Scientist Office SPHSU18Department of Health 11/3005/40Medical Research Council 1732344Medical Research Council MC_UU_00022/3Medical Research Council MC_UU_12017/11Medical Research Council MC_UU_12017/13Medical Research Council MC_UU_12017/14Medical Research Council MC_UU_12017/15
6 · The paper itself

Abstract

backgroundDirected 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)'.

methodsESC-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.

conclusionsESC-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.

Indexed as

Confounding Factors, EpidemiologicBiasBiomedical ResearchCausalityData Interpretation, StatisticalHumansModels, Statisticalcounterfactual causal inferenceDirected acyclic graphsevidence synthesisresearch methods

Identifiers

PMID31325312
PMCPMC7124493

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.