review Open AccessTop 1% cited
Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations
International Journal of Epidemiology · 2020 · Vol. 50(2) · pp. 620–632
Peter W. G. Tennant✉(Turing Institute)Eleanor J. Murray(Boston University)Kellyn F Arnold(University of Leeds)Laurie Berrie(University of Leeds)Matthew P. Fox(Boston University)Sarah Gadd(University of Leeds)Wendy J. Harrison(University of Leeds)Claire Keeble(University of Leeds)Lynsie R Ranker(Boston University)Johannes Textor(Radboud University Nijmegen)Georgia D Tomova(Turing Institute)Mark S. Gilthorpe(Turing Institute)George T. H. Ellison(University of Leeds)
Abstract
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.
Advanced Causal Inference TechniquesHealth Systems, Economic Evaluations, Quality of LifePsychometric Methodologies and TestingDirected acyclic graphConfoundingInterquartile rangeMedicineMEDLINEMathematicsCombinatoricsInternal medicineChemistry
MeSH terms
Data Interpretation, StatisticalHumansResearchBiasCausalityConfounding Factors, Epidemiologic
Funding
- Alan Turing Institute
- University of Exeter
- University of Leeds
- Medical Research Council
- Economic and Social Research Council
Citations
937
FWCI
80.84
field-weighted impact
References
39
Percentile
100%
vs. same field & year
Citations per year
References
The Table 2 Fallacy: Presenting and Interpreting Confounder and Modifier Coefficients
American Journal of Epidemiology · 2013 · 1,212 citations
Understanding and misunderstanding randomized controlled trials
Social Science & Medicine · 2017 · 1,792 citations
Robust causal inference using directed acyclic graphs: the R package ‘dagitty’
International Journal of Epidemiology · 2016 · 2,257 citations
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