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DC Field | Value | Language |
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dc.contributor.author | McKenzie, Dean | - |
dc.contributor.author | Thomas, Christopher | - |
dc.date.accessioned | 2020-04-27T00:15:03Z | - |
dc.date.available | 2020-04-27T00:15:03Z | - |
dc.date.issued | 2020-04 | - |
dc.identifier.citation | Eur J Clin Invest. 2020 Apr 20:e13249 | en_US |
dc.identifier.issn | 1365-2362 | en_US |
dc.identifier.issn | 0014-2972 | en_US |
dc.identifier.uri | http://hdl.handle.net/11434/1864 | - |
dc.description.abstract | AIM: Relative risks and odds ratios are widely reported in the medical literature, but the latter can be very difficult to understand. We sought to further clarify these important indices. METHODS: We defined both relative risks and odds ratios, then looked at the types of study for which each statistic is suited. We illustrated calculation of relative risks and odds ratios through analysis of tabled data from a recent published longitudinal study, using a 2x2 table, bar charts and R, the open source statistical programming language. Simple rules for when and how to use relative risks and odds ratios are presented. CONCLUSION: Understanding the difference between relative risks and odds ratios and when and how to use them may aid clinical interpretation, dissemination and translation of research findings. | en_US |
dc.publisher | Wiley | en_US |
dc.subject | Clinical Research | en_US |
dc.subject | Clinical Interpretation | en_US |
dc.subject | Health Promotion and Prevention | en_US |
dc.subject | Communication | en_US |
dc.subject | Statistics | en_US |
dc.subject | Relative Risks | en_US |
dc.subject | Odds Ratios | en_US |
dc.subject | Research Methods | en_US |
dc.subject | Research Translation | en_US |
dc.subject | Epworth HealthCare | en_US |
dc.title | Relative risks and odds ratios: simple rules on when and how to use them. | en_US |
dc.type | Journal Article | en_US |
dc.identifier.doi | 10.1111/eci.13249 | en_US |
dc.identifier.journaltitle | European Journal of Clinical Investigation | en_US |
dc.description.pubmeduri | https://www.ncbi.nlm.nih.gov/pubmed/32311087 | en_US |
dc.description.affiliates | Department of Statistics, Data Science and Epidemiology, Swinburne University of Technology, Melbourne, Australia | en_US |
dc.type.contenttype | Text | en_US |
Appears in Collections: | Pre-Clinical |
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