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Reflection on modern methods: constructing directed acyclic graphs (DAGs) with domain experts for health services research

Directed acyclic graphs (DAGs) are a useful tool to represent, in a graphical format, researchers’ assumptions about the causal structure among variables while providing a rationale for the choice of confounding variables to adjust for. With origins in the field of probabilistic graphical modelling,...

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Autores principales: Rodrigues, Daniela, Kreif, Noemi, Lawrence-Jones, Anna, Barahona, Mauricio, Mayer, Erik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9365627/
https://www.ncbi.nlm.nih.gov/pubmed/35713577
http://dx.doi.org/10.1093/ije/dyac135
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author Rodrigues, Daniela
Kreif, Noemi
Lawrence-Jones, Anna
Barahona, Mauricio
Mayer, Erik
author_facet Rodrigues, Daniela
Kreif, Noemi
Lawrence-Jones, Anna
Barahona, Mauricio
Mayer, Erik
author_sort Rodrigues, Daniela
collection PubMed
description Directed acyclic graphs (DAGs) are a useful tool to represent, in a graphical format, researchers’ assumptions about the causal structure among variables while providing a rationale for the choice of confounding variables to adjust for. With origins in the field of probabilistic graphical modelling, DAGs are yet to be widely adopted in applied health research, where causal assumptions are frequently made for the purpose of evaluating health services initiatives. In this context, there is still limited practical guidance on how to construct and use DAGs. Some progress has recently been made in terms of building DAGs based on studies from the literature, but an area that has received less attention is how to create DAGs from information provided by domain experts, an approach of particular importance when there is limited published information about the intervention under study. This approach offers the opportunity for findings to be more robust and relevant to patients, carers and the public, and more likely to inform policy and clinical practice. This article draws lessons from a stakeholder workshop involving patients, health care professionals, researchers, commissioners and representatives from industry, whose objective was to draw DAGs for a complex intervention—online consultation, i.e. written exchange between the patient and health care professional using an online system—in the context of the English National Health Service. We provide some initial, practical guidance to those interested in engaging with domain experts to develop DAGs.
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spelling pubmed-93656272022-08-11 Reflection on modern methods: constructing directed acyclic graphs (DAGs) with domain experts for health services research Rodrigues, Daniela Kreif, Noemi Lawrence-Jones, Anna Barahona, Mauricio Mayer, Erik Int J Epidemiol Education Corner Directed acyclic graphs (DAGs) are a useful tool to represent, in a graphical format, researchers’ assumptions about the causal structure among variables while providing a rationale for the choice of confounding variables to adjust for. With origins in the field of probabilistic graphical modelling, DAGs are yet to be widely adopted in applied health research, where causal assumptions are frequently made for the purpose of evaluating health services initiatives. In this context, there is still limited practical guidance on how to construct and use DAGs. Some progress has recently been made in terms of building DAGs based on studies from the literature, but an area that has received less attention is how to create DAGs from information provided by domain experts, an approach of particular importance when there is limited published information about the intervention under study. This approach offers the opportunity for findings to be more robust and relevant to patients, carers and the public, and more likely to inform policy and clinical practice. This article draws lessons from a stakeholder workshop involving patients, health care professionals, researchers, commissioners and representatives from industry, whose objective was to draw DAGs for a complex intervention—online consultation, i.e. written exchange between the patient and health care professional using an online system—in the context of the English National Health Service. We provide some initial, practical guidance to those interested in engaging with domain experts to develop DAGs. Oxford University Press 2022-06-17 /pmc/articles/PMC9365627/ /pubmed/35713577 http://dx.doi.org/10.1093/ije/dyac135 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of the International Epidemiological Association. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Education Corner
Rodrigues, Daniela
Kreif, Noemi
Lawrence-Jones, Anna
Barahona, Mauricio
Mayer, Erik
Reflection on modern methods: constructing directed acyclic graphs (DAGs) with domain experts for health services research
title Reflection on modern methods: constructing directed acyclic graphs (DAGs) with domain experts for health services research
title_full Reflection on modern methods: constructing directed acyclic graphs (DAGs) with domain experts for health services research
title_fullStr Reflection on modern methods: constructing directed acyclic graphs (DAGs) with domain experts for health services research
title_full_unstemmed Reflection on modern methods: constructing directed acyclic graphs (DAGs) with domain experts for health services research
title_short Reflection on modern methods: constructing directed acyclic graphs (DAGs) with domain experts for health services research
title_sort reflection on modern methods: constructing directed acyclic graphs (dags) with domain experts for health services research
topic Education Corner
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9365627/
https://www.ncbi.nlm.nih.gov/pubmed/35713577
http://dx.doi.org/10.1093/ije/dyac135
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