Reflection on modern methods : constructing directed acyclic graphs (DAGs) with domain experts for health services research
© The Author(s) 2022. Published by Oxford University Press on behalf of the International Epidemiological Association..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:51 |
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| Enthalten in: |
International journal of epidemiology - 51(2022), 4 vom: 10. Aug., Seite 1339-1348 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Rodrigues, Daniela [VerfasserIn] |
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| Links: |
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| Themen: |
Causal inference |
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| Anmerkungen: |
Date Completed 12.08.2022 Date Revised 19.10.2022 published: Print Citation Status MEDLINE |
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| doi: |
10.1093/ije/dyac135 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
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| 520 | |a © The Author(s) 2022. Published by Oxford University Press on behalf of the International Epidemiological Association. | ||
| 520 | |a 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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| 700 | 1 | |a Lawrence-Jones, Anna |e verfasserin |4 aut | |
| 700 | 1 | |a Barahona, Mauricio |e verfasserin |4 aut | |
| 700 | 1 | |a Mayer, Erik |e verfasserin |4 aut | |
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