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dc.contributor.authorAndrew, BYen_US
dc.contributor.authorAlan Brookhart, Men_US
dc.contributor.authorPearse, Ren_US
dc.contributor.authorRaghunathan, Ken_US
dc.contributor.authorKrishnamoorthy, Ven_US
dc.date.accessioned2023-08-03T10:32:34Z
dc.date.available2023-06-24en_US
dc.date.issued2023-07-20en_US
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/89966
dc.description.abstractCausal inference in observational research requires a careful approach to adjustment for confounding. One such approach is the use of propensity score analyses. In this editorial, we focus on the role of propensity score-based methods in estimating causal effects from non-randomised observational data. We highlight the details, assumptions, and limitations of these methods and provide authors with guidelines for the conduct and reporting of propensity score analyses.en_US
dc.languageengen_US
dc.relation.ispartofBr J Anaesthen_US
dc.subjectcausal inferenceen_US
dc.subjectconfoundingen_US
dc.subjectobservational researchen_US
dc.subjectpropensity score methodsen_US
dc.subjectstatisticsen_US
dc.titlePropensity score methods in observational research: brief review and guide for authors.en_US
dc.typeArticle
dc.identifier.doi10.1016/j.bja.2023.06.054en_US
pubs.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/37481434en_US
pubs.notesNot knownen_US
pubs.publication-statusPublished onlineen_US
dcterms.dateAccepted2023-06-24en_US


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