Causal inference in perioperative medicine observational research: part 1, a graphical introduction.

Biomedical Research / methods Humans Models, Statistical Observational Studies as Topic / methods Perioperative Medicine / methods
causal inference confounding epidemiology graphical models observational research

Journal

British journal of anaesthesia
ISSN: 1471-6771
Titre abrégé: Br J Anaesth
Pays: England
ID NLM: 0372541

Informations de publication

Date de publication:
09 2020
Historique:
received: 22 08 2019
revised: 28 02 2020
accepted: 17 03 2020
pubmed: 1 7 2020
medline: 17 9 2020
entrez: 1 7 2020
Statut: ppublish

Résumé

Graphical models have emerged as a tool to map out the interplay between multiple measured and unmeasured variables, and can help strengthen the case for a causal association between exposures and outcomes in observational studies. In Part 1 of this methods series, we will introduce the reader to graphical models for causal inference in perioperative medicine, and set the framework for Part 2 of the series involving advanced methods for causal inference.

Identifiants

DOI: 10.1016/j.bja.2020.03.031 PMID: 32600803
pubmed: 32600803
pii: S0007-0912(20)30293-2
doi: 10.1016/j.bja.2020.03.031
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

393-397

Commentaires et corrections

Type : CommentIn

Informations de copyright

Copyright © 2020 British Journal of Anaesthesia. All rights reserved.

Auteurs

Vijay Krishnamoorthy (V)

Critical Care and Perioperative Epidemiologic Research (CAPER) Unit, Department of Anesthesiology, Duke University Hospital, Durham, NC, USA. Electronic address: vijay.krishnamoorthy@duke.edu.

Danny J N Wong (DJN)

Department of Anaesthesia, Guy's and St Thomas' NHS Foundation Trust, London, UK.

Matt Wilson (M)

Critical Care, University College London Hospitals NHS Foundation Trust, London, UK.

Karthik Raghunathan (K)

Critical Care and Perioperative Epidemiologic Research (CAPER) Unit, Department of Anesthesiology, Duke University Hospital, Durham, NC, USA; Anesthesiology Service, Durham Veterans Affairs Medical Center, Durham, NC, USA.

Tetsu Ohnuma (T)

Critical Care and Perioperative Epidemiologic Research (CAPER) Unit, Department of Anesthesiology, Duke University Hospital, Durham, NC, USA.

Duncan McLean (D)

Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

S Ramani Moonesinghe (SR)

Critical Care, University College London Hospitals NHS Foundation Trust, London, UK.

Steve K Harris (SK)

Critical Care, University College London Hospitals NHS Foundation Trust, London, UK.

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Classifications MeSH

questionsmedicales.fr › Disciplines des sciences naturelles › Science › Recherche › Recherche biomédicale › Causal inference in perioperative medicine...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Causal inference in perioperative medicine...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Statistiques comme sujet › Modèles statistiques › Causal inference in perioperative medicine...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Caractéristiques des études épidémiologiques › Études cliniques comme sujet › Études observationnelles comme sujet › Causal inference in perioperative medicine...
questionsmedicales.fr › Professions de santé › Médecine › Médecine périopératoire › Causal inference in perioperative medicine...