dc.contributor.author | Andrew, BY | en_US |
dc.contributor.author | Alan Brookhart, M | en_US |
dc.contributor.author | Pearse, R | en_US |
dc.contributor.author | Raghunathan, K | en_US |
dc.contributor.author | Krishnamoorthy, V | en_US |
dc.date.accessioned | 2023-08-03T10:32:34Z | |
dc.date.available | 2023-06-24 | en_US |
dc.date.issued | 2023-07-20 | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/89966 | |
dc.description.abstract | Causal 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.language | eng | en_US |
dc.relation.ispartof | Br J Anaesth | en_US |
dc.subject | causal inference | en_US |
dc.subject | confounding | en_US |
dc.subject | observational research | en_US |
dc.subject | propensity score methods | en_US |
dc.subject | statistics | en_US |
dc.title | Propensity score methods in observational research: brief review and guide for authors. | en_US |
dc.type | Article | |
dc.identifier.doi | 10.1016/j.bja.2023.06.054 | en_US |
pubs.author-url | https://www.ncbi.nlm.nih.gov/pubmed/37481434 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published online | en_US |
dcterms.dateAccepted | 2023-06-24 | en_US |