The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations
Open accessAbstract
Background: 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. Methods: Original 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. Results: A 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). Conclusion: There 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.
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Cites
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“This is arguably unnecessary since omitting an arc invokes the greater assumption, because no arc between two variables declares there is no causal relationship between the two variables, regardless of direction, sign, strength or parametric form.(12) Increasing the number of arcs does however bring important practica…”
BackgroundInfluentialAir Pollution and Hospital Admissions for Heart Disease in Eight U.S. Counties
Schwartz, J. (1999) · Epidemiology · doi:10.1097/00001648-199901000-00005 · Cited by 3,038
Robust causal inference using directed acyclic graphs: the R package ‘dagitty’
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“Data were extracted on the reporting of: estimands, DAGs and adjustment sets, alongside the characteristics of each article’s largest DAG.”
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Maternal Pesticide Exposure from Multiple Sources and Selected Congenital Anomalies
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“This is exemplified by the ‘Table 2 fallacy’, which occurs when the coefficients for two or more ‘risk factors’ in a multivariable regression model are (mistakenly) interpreted as estimates for meaningful causal effects.(8) Causal inference approaches, such as the potential outcomes framework, promote greater transpar…”
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Cited by
Conformal Inference of Counterfactuals and Individual Treatment Effects
Lei, L., & Candès, E. J. (2021) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/rssb.12445 · Cited by 166
Triangulating Evidence through the Inclusion of Genetically Informed Designs
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Testing graphical causal models using the R package “dagitty”
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Self-citationHuman brain anatomy reflects separable genetic and environmental components of socioeconomic status
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Metadata
- Authors
- Tennant, P. W. G., Murray, E. J., Arnold, K. F., Berrie, L., Fox, M. P., Gadd, S. C., Harrison, W. J., Keeble, C., Ranker, L. R., Textor, J., Tomova, G. D., Gilthorpe, M. S., & Ellison, G. T. H.
- Venue
- International Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-12-17
- Volume
- 50
- Issue
- 2
- Pages
- 620-632
- ISSN
- 0300-5771 (ISSN-L)
- 1464-3685
- OpenAlex
- W3114319529
- Cited by
- 1,052
- References
- 37
- FWCI
- 61.50
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Tennant, P. W. G., Murray, E. J., Arnold, K. F., Berrie, L., Fox, M. P., Gadd, S. C., Harrison, W. J., Keeble, C., Ranker, L. R., Textor, J., Tomova, G. D., Gilthorpe, M. S., & Ellison, G. T. H. (2021). Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations. International Journal of Epidemiology. https://doi.org/10.1093/ije/dyaa213
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