Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations
| Autor: | Laurie Berrie, George T. H. Ellison, Eleanor J Murray, Kellyn F Arnold, Georgia D Tomova, Peter W. G. Tennant, Wendy J Harrison, Sarah C. Gadd, Johannes Textor, Matthew P. Fox, Mark S. Gilthorpe, Claire Keeble, Lynsie R Ranker |
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| Rok vydání: | 2021 |
| Předmět: |
Web of science
Epidemiology Cancer development and immune defence Radboud Institute for Molecular Life Sciences [Radboudumc 2] MEDLINE 030209 endocrinology & metabolism causal diagrams 03 medical and health sciences 0302 clinical medicine Bias Statistics Methods Humans graphical model theory AcademicSubjects/MED00860 030212 general & internal medicine causal inference observational studies Research review Mathematics covariate adjustment H990 Research Causal effect Confounding Confounding Factors Epidemiologic General Medicine Directed acyclic graph confounding Causality Directed acyclic graphs reporting practices Estimand Causal inference Data Interpretation Statistical |
| Zdroj: | International Journal of Epidemiology, 50, 2, pp. 620-632 International Journal of Epidemiology International Journal of Epidemiology, 50, 620-632 |
| ISSN: | 0300-5771 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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