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

P. W. G. Tennant | Zendy; Eleanor J. Murray | Zendy; Kellyn F Arnold | Zendy; Laurie Berrie | Zendy; Matthew P. Fox | Zendy; Sarah Gadd | Zendy; Wendy J. Harrison | Zendy; Claire Keeble | Zendy; Lynsie R. Ranker | Zendy; Johannes Textor | Zendy; Georgia D Tomova | Zendy; Mark S. Gilthorpe | Zendy; George T. H. Ellison | Zendy

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

Author(s) -

P. W. G. Tennant,

Eleanor J. Murray,

Kellyn F Arnold,

Laurie Berrie,

Matthew P. Fox,

Sarah Gadd,

Wendy J. Harrison,

Claire Keeble,

Lynsie R. Ranker,

Johannes Textor,

Georgia D Tomova,

Mark S. Gilthorpe,

George T. H. Ellison

Publication year - 2020

Publication title -

international journal of epidemiology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.406

H-Index - 208

eISSN - 1464-3685

pISSN - 0300-5771

DOI - 10.1093/ije/dyaa213

Subject(s) - directed acyclic graph , confounding , interquartile range , medicine , medline , mathematics , combinatorics , chemistry , biochemistry

Directed 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.

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