IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0281259.html
   My bibliography  Save this article

Directed acyclic graphs in perioperative observational research–A systematic review and critique against best practice recommendations

Author

Listed:
  • Matthew Lamont Watson
  • Sebastian H M Hickman
  • Kaya Marlen Dreesbeimdiek
  • Katharina Kohler
  • Daniel J Stubbs

Abstract

The Directed Acyclic Graph (DAG) is a graph representing causal pathways for informing the conduct of an observational study. The use of DAGs allows transparent communication of a causal model between researchers and can prevent over-adjustment biases when conducting causal inference, permitting greater confidence and transparency in reported causal estimates. In the era of ‘big data’ and increasing number of observational studies, the role of the DAG is becoming more important. Recent best-practice guidance for constructing a DAG with reference to the literature has been published in the ‘Evidence synthesis for constructing DAGs’ (ESC-DAG) protocol. We aimed to assess adherence to these principles for DAGs constructed within perioperative literature. Following registration on the International Prospective Register of Systematic Reviews (PROSPERO) and with adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting framework for systematic reviews, we searched the Excerpta Medica dataBASE (Embase), the Medical Literature Analysis and Retrieval System Online (MEDLINE) and Cochrane databases for perioperative observational research incorporating a DAG. Nineteen studies were included in the final synthesis. No studies demonstrated any evidence of following the mapping stage of the protocol. Fifteen (79%) fulfilled over half of the translation and integration one stages of the protocol. Adherence with one stage did not guarantee fulfilment of the other. Two studies (11%) undertook the integration two stage. Unmeasured variables were handled inconsistently between studies. Only three (16%) studies included unmeasured variables within their DAG and acknowledged their implication within the main text. Overall, DAGs that were constructed for use in perioperative observational literature did not consistently adhere to best practice, potentially limiting the benefits of subsequent causal inference. Further work should focus on exploring reasons for this deviation and increasing methodological transparency around DAG construction.

Suggested Citation

  • Matthew Lamont Watson & Sebastian H M Hickman & Kaya Marlen Dreesbeimdiek & Katharina Kohler & Daniel J Stubbs, 2023. "Directed acyclic graphs in perioperative observational research–A systematic review and critique against best practice recommendations," PLOS ONE, Public Library of Science, vol. 18(2), pages 1-16, February.
    • Handle: RePEc:plo:pone00:0281259
    DOI: 10.1371/journal.pone.0281259
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0281259
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0281259&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0281259?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Deaton, Angus & Cartwright, Nancy, 2018. "Understanding and misunderstanding randomized controlled trials," Social Science & Medicine, Elsevier, vol. 210(C), pages 2-21.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Perrotta, Manuela & Geampana, Alina, 2020. "The trouble with IVF and randomised control trials: Professional legitimation narratives on time-lapse imaging and evidence-informed care," Social Science & Medicine, Elsevier, vol. 258(C).
    2. Christopher J. Ruhm, 2019. "Shackling the Identification Police?," Southern Economic Journal, John Wiley & Sons, vol. 85(4), pages 1016-1026, April.
    3. Omar Al-Ubaydli & John List & Claire Mackevicius & Min Sok Lee & Dana Suskind, 2019. "How Can Experiments Play a Greater Role in Public Policy? 12 Proposals from an Economic Model of Scaling," Artefactual Field Experiments 00679, The Field Experiments Website.
    4. Martin, Will, 2021. "Tools for measuring the full impacts of agricultural interventions," IFPRI-MCC technical papers 2, International Food Policy Research Institute (IFPRI).
    5. Andor, Mark A. & Gerster, Andreas & Peters, Jörg & Schmidt, Christoph M., 2020. "Social Norms and Energy Conservation Beyond the US," Journal of Environmental Economics and Management, Elsevier, vol. 103(C).
    6. Kabeer, Naila, 2020. "‘Misbehaving’ RCTs: The confounding problem of human agency," World Development, Elsevier, vol. 127(C).
    7. Cristina Bellés-Obrero & María Lombardi, 2022. "Teacher Performance Pay and Student Learning: Evidence from a Nationwide Program in Peru," Economic Development and Cultural Change, University of Chicago Press, vol. 70(4), pages 1631-1669.
    8. Sophie van Huellen & Duo Qin, 2019. "Compulsory Schooling and Returns to Education: A Re-Examination," Econometrics, MDPI, vol. 7(3), pages 1-20, September.
    9. Marchionni, Caterina & Reijula, Samuli, 2018. "What is mechanistic evidence, and why do we need it for evidence-based policy?," SocArXiv 4ufbm, Center for Open Science.
    10. Bayer, Patrick & Kennedy, Ryan & Yang, Joonseok & Urpelainen, Johannes, 2020. "The need for impact evaluation in electricity access research," Energy Policy, Elsevier, vol. 137(C).
    11. Jesus Fernandez-Villaverde, 2020. "Simple Rules for a Complex World with Arti?cial Intelligence," PIER Working Paper Archive 20-010, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    12. Vellore Arthi & James Fenske, 2018. "Polygamy and child mortality: Historical and modern evidence from Nigeria’s Igbo," Review of Economics of the Household, Springer, vol. 16(1), pages 97-141, March.
    13. Ravallion, Martin, 2020. "Highly prized experiments," World Development, Elsevier, vol. 127(C).
    14. Irina Gemmo & Pierre-Carl Michaud & Olivia S. Mitchell, 2023. "Selection into Financial Education and Effects on Portfolio Choice," NBER Working Papers 31682, National Bureau of Economic Research, Inc.
    15. Paige Seitz & Robert Strong & Steve Hague & Theresa P. Murphrey, 2022. "Evaluating Agricultural Extension Agent’s Sustainable Cotton Land Production Competencies: Subject Matter Discrepancies Restricting Farmers’ Information Adoption," Land, MDPI, vol. 11(11), pages 1-17, November.
    16. Haoge Chang & Joel Middleton & P. M. Aronow, 2021. "Exact Bias Correction for Linear Adjustment of Randomized Controlled Trials," Papers 2110.08425, arXiv.org, revised Oct 2021.
    17. Andreas C Drichoutis & Rodolfo M Nayga, 2020. "Economic Rationality under Cognitive Load," The Economic Journal, Royal Economic Society, vol. 130(632), pages 2382-2409.
    18. Wallin, Annika & Wahlberg, Lena & Persson, Johannes & Dewitt, Barry, 2020. "“Science and proven experience”: How should the epistemology of medicine inform the regulation of healthcare?," Health Policy, Elsevier, vol. 124(8), pages 842-848.
    19. Wang Ning, 2018. "Law and the Economy: An Introduction to Coasian Law and Economics," Man and the Economy, De Gruyter, vol. 5(2), pages 1-13, December.
    20. Cristina Corduneanu-Huci & Michael T. Dorsch & Paul Maarek, 2017. "Learning to constrain: Political competition and randomized controlled trials in development," THEMA Working Papers 2017-24, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0281259. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.