Evidence map›Paper›PMID 33330936›Full record

ReviewInternational journal of epidemiology2021

Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations.

Peter W G Tennant, Eleanor J Murray, Kellyn F Arnold, Laurie Berrie, Matthew P Fox, Sarah C Gadd, Wendy J Harrison, Claire Keeble, Lynsie R Ranker, Johannes Textor and 3 more

Abstract readReview
In one paragraph

Review in International journal of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 527 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
527citing papers in PubMed, 3 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

527 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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  4. Trial
  5. Risk of Tuberculosis Infection in Young Children Exposed to Multidrug-resistant Tuberculosis in the TB-CHAMP Multi-site Randomized Controlled Trial.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2025
    Trial
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  8. Body mass index and hospital mortality in mechanically ventilated patients with acute hypoxaemic respiratory failure: A retrospective cohort study.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2026
    Article
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  17. Did You Know That Confounding Could Bias Results Obtained within an Economic Evaluation?Medical decision making : an international journal of the Society for Medical Decision Making · 2026
    Article
  18. Review
  19. Article
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467 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Peter W G TennantLeeds Institute for Data Analytics, University of Leeds, Leeds, UK.ORCID 0000-0003-1555-069X
Eleanor J MurrayDepartment of Epidemiology, School of Public Health, Boston University, Boston, MA, USA.
Kellyn F ArnoldLeeds Institute for Data Analytics, University of Leeds, Leeds, UK.ORCID 0000-0002-0911-5029
Laurie BerrieLeeds Institute for Data Analytics, University of Leeds, Leeds, UK.
Matthew P FoxDepartment of Epidemiology, School of Public Health, Boston University, Boston, MA, USA.
Sarah C GaddLeeds Institute for Data Analytics, University of Leeds, Leeds, UK.
Wendy J HarrisonLeeds Institute for Data Analytics, University of Leeds, Leeds, UK.ORCID 0000-0003-1180-8112
Claire KeebleLeeds Institute for Data Analytics, University of Leeds, Leeds, UK.
Lynsie R RankerDepartment of Epidemiology, School of Public Health, Boston University, Boston, MA, USA.
Johannes TextorDepartment of Tumour Immunology, Radboud University Medical Center, Nijmegen, The Netherlands.ORCID 0000-0002-0459-9458
Georgia D TomovaLeeds Institute for Data Analytics, University of Leeds, Leeds, UK.
Mark S GilthorpeLeeds Institute for Data Analytics, University of Leeds, Leeds, UK.
George T H EllisonLeeds Institute for Data Analytics, University of Leeds, Leeds, UK.

Funding

Medical Research Council MR/K501402/1
6 · The paper itself

Abstract

backgroundDirected 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.

methodsOriginal 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.

resultsA 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).

conclusionThere 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.

Indexed as

ResearchBiasCausalityConfounding Factors, EpidemiologicData Interpretation, StatisticalHumanscausal diagramscausal inferenceconfoundingcovariate adjustmentDirected acyclic graphsgraphical model theoryobservational studiesreporting practices

Identifiers

PMID33330936
PMCPMC8128477

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.