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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.
The chart portion of the forest plot will be on the right hand side and will indicate the mean difference in effect between the test and control groups in the studies. A more precise rendering of the data shows up in number form in the text of each line, while a somewhat less precise graphic representation shows up in chart form on the right.
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 ...
Both the relative risk and odds ratio are relevant in retrospective cohort studies, but only the odds ratio can be used in case-control studies. Although most case-control studies are retrospective, they can also be prospective when the researcher still enrolls participants based on the occurrence of a disease as new cases occur. [citation needed]
The simplest measure of association for a 2 × 2 contingency table is the odds ratio. Given two events, A and B, the odds ratio is defined as the ratio of the odds of A in the presence of B and the odds of A in the absence of B, or equivalently (due to symmetry), the ratio of the odds of B in the presence of A and the odds of B in the absence 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
It is defined as the inverse of the absolute risk increase, and computed as / (), where is the incidence in the treated (exposed) group, and is the incidence in the control (unexposed) group. [1] Intuitively, the lower the number needed to harm, the worse the risk factor, with 1 meaning that every exposed person is harmed.
Post-test odds given by multiplying pretest odds with the ratio: Theoretically limitless: Pre-test state (and thus the pre-test probability) does not have to be same as in reference group: By relative risk: Quotient of risk among exposed and risk among unexposed: Pre-test probability multiplied by the relative risk