Noncollapsibility of the odds ratio unraveled
Purpose of review The concept of noncollapsibility of the odds ratio has intrigued the epidemiology community for decades. Despite often being overlooked or negatively perceived, which can lead researchers to prefer collapsible measures, this study highlights the beneficial aspects of noncollapsibil...
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Published in | Current Opinion in Epidemiology and Public Health |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
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08.08.2023
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Abstract | Purpose of review The concept of noncollapsibility of the odds ratio has intrigued the epidemiology community for decades. Despite often being overlooked or negatively perceived, which can lead researchers to prefer collapsible measures, this study highlights the beneficial aspects of noncollapsibility of the odds ratio. Recent findings This study links recent diagnostic test research with effect measures. Noncollapsibility is a useful characteristic, and its understanding can have important implications in medical research. Summary We demonstrate that a change in effect could be due to confounding, noncollapsibility, or a combination of both and that noncollapsibility can be a powerful tool for data analysis and interpretation when properly understood. This study aims to help researchers demystify this complex issue and offers updated recommendations. |
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AbstractList | Purpose of review The concept of noncollapsibility of the odds ratio has intrigued the epidemiology community for decades. Despite often being overlooked or negatively perceived, which can lead researchers to prefer collapsible measures, this study highlights the beneficial aspects of noncollapsibility of the odds ratio. Recent findings This study links recent diagnostic test research with effect measures. Noncollapsibility is a useful characteristic, and its understanding can have important implications in medical research. Summary We demonstrate that a change in effect could be due to confounding, noncollapsibility, or a combination of both and that noncollapsibility can be a powerful tool for data analysis and interpretation when properly understood. This study aims to help researchers demystify this complex issue and offers updated recommendations. |
Author | Abdulmajeed, Jazeel Kostoulas, Polychronis Doi, Suhail A.R. Shi, Zumin |
Author_xml | – sequence: 1 givenname: Jazeel surname: Abdulmajeed fullname: Abdulmajeed, Jazeel organization: Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar – sequence: 2 givenname: Polychronis surname: Kostoulas fullname: Kostoulas, Polychronis organization: Laboratory of Epidemiology and Artificial Intelligence, Faculty of Public Health, University of Thessaly, Thessaly, Greece – sequence: 3 givenname: Zumin surname: Shi fullname: Shi, Zumin organization: Department of Human Nutrition, College of Health Sciences, QU Health, Qatar University, Doha, Qatar – sequence: 4 givenname: Suhail A.R. surname: Doi fullname: Doi, Suhail A.R. organization: Department of Population Medicine, College of Medicine, QU Health, Qatar University, Doha, Qatar |
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Cites_doi | 10.1177/0962280213505804 10.1016/j.jclinepi.2021.06.004 10.1214/ss/1009211805 10.1002/sim.1099 10.1111/jebm.12495 10.1093/oxfordjournals.aje.a114593 10.1002/bimj.202000202 10.1016/j.jclinepi.2020.08.019 10.1177/09622802211034219 10.1016/S0002-8703(00)90001-2 10.1016/j.amjcard.2012.09.002 10.3310/hta5330 10.1097/00001648-199701000-00002 10.1080/01621459.2016.1192546 10.1016/j.jclinepi.2021.08.003 10.2188/jea.JE20130062 10.1186/s12874-021-01316-8 10.1002/bimj.201900297 10.1016/j.jclinepi.2021.06.007 10.1093/aje/kwaa267 10.1186/s12874-020-01145-1 10.1002/sim.6739 |
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