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

Tennant, PWG orcid.org/0000-0003-1555-069X, Murray, EJ, Arnold, KF orcid.org/0000-0002-0911-5029 et al. (10 more authors) (2021) Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations. International Journal of Epidemiology, 50 (2). pp. 620-632. ISSN 0300-5771

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Item Type: Article
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© The Author(s) 2020. This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/)

Keywords: Directed acyclic graphs, graphical model theory, causal diagrams, causal inference, observational studies, confounding, covariate adjustment, reporting practices
Dates:
  • Published: April 2021
  • Published (online): 17 December 2020
  • Accepted: 12 October 2020
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Centre for Spatial Analysis & Policy (Leeds)
The University of Leeds > Faculty of Medicine and Health (Leeds) > Medicine & Health Faculty Office (Leeds) > Faculty Office Functions (FOMH) (Leeds) > Dean's Office (FOMH) (Leeds)
The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM) > Clinical & Population Science Dept (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 03 Dec 2021 14:20
Last Modified: 03 Dec 2021 14:20
Status: Published
Publisher: Oxford University Press (OUP)
Identification Number: 10.1093/ije/dyaa213
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