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The dawn of directed acyclic graphs in primary care research and education

The dawn of directed acyclic graphs in primary care research and education To the Editor,Primary care research has been developing daily and addresses diseases and various fields involving local patients, communities, and practices to measure health improvements. In designing these studies, causal directed acyclic graphs (DAGs) are still novel for primary care researchers. DAGs comprise variable names and arrows following several rules and allow researchers to show their research concepts and causal relationship explaining bias structures, as well as confounding. Appropriate utilization of DAGs helps us visually distinguish between factors that require adjustment, such as confounders and bias, and those that do not.1Historically, DAGs are based on mathematical graph theory.1 More recently, DAGs have helped evaluate causal relationships in epidemiology and medicine.1 In estimating causal effects, the identification of adjustment variables using DAGs is beneficial for multivariable regression and other methods. DAGs can identify selection bias at the study design stage and observation period. Moreover, DAGs detect measurement bias, such as detection bias, and explore variables even in risk prediction regression models.1–4Some reviews and tutorials also introduced DAGs to clinical researchers.1,5 Various research fields, including circulation, respiratory medicine, pediatrics, and psychiatry, recently mobilized DAGs. We searched the numbers of annual published articles mentioning DAGs in their title or abstract with PubMed http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of General and Family Medicine Wiley

The dawn of directed acyclic graphs in primary care research and education

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Publisher
Wiley
Copyright
© 2023 Japan Primary Care Association
eISSN
2189-7948
DOI
10.1002/jgf2.627
Publisher site
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Abstract

To the Editor,Primary care research has been developing daily and addresses diseases and various fields involving local patients, communities, and practices to measure health improvements. In designing these studies, causal directed acyclic graphs (DAGs) are still novel for primary care researchers. DAGs comprise variable names and arrows following several rules and allow researchers to show their research concepts and causal relationship explaining bias structures, as well as confounding. Appropriate utilization of DAGs helps us visually distinguish between factors that require adjustment, such as confounders and bias, and those that do not.1Historically, DAGs are based on mathematical graph theory.1 More recently, DAGs have helped evaluate causal relationships in epidemiology and medicine.1 In estimating causal effects, the identification of adjustment variables using DAGs is beneficial for multivariable regression and other methods. DAGs can identify selection bias at the study design stage and observation period. Moreover, DAGs detect measurement bias, such as detection bias, and explore variables even in risk prediction regression models.1–4Some reviews and tutorials also introduced DAGs to clinical researchers.1,5 Various research fields, including circulation, respiratory medicine, pediatrics, and psychiatry, recently mobilized DAGs. We searched the numbers of annual published articles mentioning DAGs in their title or abstract with PubMed

Journal

Journal of General and Family Medicine – Wiley

Published: Jul 1, 2023

References

  • Using causal diagrams to improve the design and interpretation of medical research
    Etminan, M; Collins, GS; Mansournia, MA
  • Directed acyclic graphs and causal thinking in clinical risk prediction modeling
    Piccininni, M; Konigorski, S; Rohmann, JL; Kurth, T
  • Causal diagrams for epidemiologic research
    Greenland, S; Pearl, J; Robins, JM
  • Modern Epidemiology
    Lash, TL; VanderWeele, TJ; Haneuse, S; Rothman, KJ
  • Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations
    Tennant, PWG; Murray, EJ; Arnold, KF; Berrie, L; Fox, MP; Gadd, SC