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An odds ratio (OR) is a statistic that quantifies the strength of the association between two events, A and B. The odds ratio is defined as the ratio of the odds of event A taking place in the presence of B, and the odds of A in the absence of B. Due to symmetry, odds ratio reciprocally calculates the ratio of the odds of B occurring in the presence of A, and the odds of B in the absence of A.
In medical testing with binary classification, the diagnostic odds ratio (DOR) is a measure of the effectiveness of a diagnostic test. [1] It is defined as the ratio of the odds of the test being positive if the subject has a disease relative to the odds of the test being positive if the subject does not have the disease.
In practice the odds ratio is commonly used for case-control studies, as the relative risk cannot be estimated. [1] In fact, the odds ratio has much more common use in statistics, since logistic regression, often associated with clinical trials, works with the log of the odds ratio, not relative risk. Because the (natural log of the) odds of a ...
and = / / = While the prevalence is only 9% (9/100), the odds ratio (OR) is equal to 11.3 and the relative risk (RR) is equal to 7.2. Despite fulfilling the rare disease assumption overall, the OR and RR can hardly be considered to be approximately the same. However, the prevalence in the exposed group is 40%, which means is not sufficiently small
3 Bayesian interpretation. ... is the incidence in the exposed group, and is the incidence ... Odds ratio: OR (EE / EN) / (CE / CN) 1.5 See also
Names of (fictional) studies are shown on the left, odds ratios and confidence intervals on the right. Wikimedia Commons has media related to Forest plots . A forest plot , also known as a blobbogram, is a graphical display of estimated results from a number of scientific studies addressing the same question, along with the overall results. [ 1 ]
The curve represents the odds of an endpoint having occurred at each point in time (the hazard). The hazard ratio is simply the relationship between the instantaneous hazards in the two groups and represents, in a single number, the magnitude of distance between the Kaplan–Meier plots. [7] Hazard ratios do not reflect a time unit of the study.
The (latter mentioned) effect of overestimation can be compensated for by converting risks to odds, and relative risks to odds ratios. However, this does not compensate for (former mentioned) effect of any difference between pre-test probability of an individual and the prevalence in the reference group.