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AuthorDoi, Suhail A.
AuthorAbdulmajeed, Jazeel
AuthorXu, Chang
Available date2023-02-21T10:15:05Z
Publication Date2022-09-01
Publication NameJournal of Evidence-Based Medicine
Identifierhttp://dx.doi.org/10.1111/jebm.12495
CitationDoi, SA, Abdulmajeed, J, Xu, C. Redefining effect modification. J Evid Based Med. 2022; 15: 192– 197. https://doi.org/10.1111/jebm.12495
ISSN1756-5383
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85138569584&origin=inward
URIhttp://hdl.handle.net/10576/40257
AbstractThe odds ratio (OR) has been misunderstood in evidence based medicine and clinical epidemiology. Currently, “noncollapsibility” is considered a problem with interpretation of the OR and it is thought that the OR is rarely the parameter of interest for causal inference or interpretation of effect modification. The current focus on the relative risk (RR) and risk difference (RD) suffers from an important limitation: they are not solely measures of effect and vary numerically with baseline risk. In this paper, generalized linear models are examined in terms of the three binary effect measures commonly used in epidemiology to demonstrate that ORs may be the only way to interpret effect modification and have properties that should make them the parameter of interest for causal inference. We look forward to discussion, debate, and counter-views on this issue from the epidemiology community.
SponsorThis work was made possible by Program Grant #NPRP-BSRA01-0406-210030 from the Qatar National Research Fund (a member of Qatar Foundation). The findings herein reflect the work and are solely the responsibility of the authors. All authors had full access to all the data in the study and the corresponding author had final responsibility for the decision to submit for publication.
Languageen
PublisherWiley
Subjecteffect modification
noncollapsibility
odds ratio
TitleRedefining effect modification
TypeArticle
Pagination192-197
Issue Number3
Volume Number15
ESSN1756-5391
dc.accessType Abstract Only


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