An<unk>  (OR) is a statistic that quantifies the strength of the association between two events, A and B. The<unk>  is defined as the<unk>  of the<unk>  of A in the presence of B and the<unk>  of A in the absence of B, or equivalently (due to symmetry), the<unk>  of the<unk>  of B in the presence of A and the<unk>  of B in the absence of A. Two events are independent if and only if the OR equals 1, i.e., the<unk>  of one event are the same in either the presence or absence of the other event. If the OR is greater than 1, then A and B are associated (correlated) in the sense that, compared to the absence of B, the presence of B raises the<unk>  of A, and symmetrically the presence of A raises the<unk>  of B. Conversely, if the OR is less than 1, then A and B are negatively correlated, and the presence of one event reduces the<unk>  of the other event. Note that the<unk>  is symmetric in the two events, and there is no causal direction implied (correlation does not imply causation): an OR greater than 1 does not establish that B causes A, or that A causes B. Two similar statistics that are often used to quantify associations are the relative risk (RR) and the absolute risk reduction (ARR). Often, the parameter of greatest interest is actually the RR, which is the<unk>  of the probabilities analogous to the<unk>  used in the OR. However, available data frequently do not allow for the computation of the RR or the ARR but do allow for the computation of the OR, as in case-control studies, as explained below. On the other hand, if one of the properties (A or B) is sufficiently rare (in epidemiology this is called the rare disease assumption), then the OR is approximately equal to the corresponding RR. The OR plays an important role in the logistic model.
Odds ratio
Hazard ratio
Risk difference
Minimal important difference
Absolute threshold
Indicative limit value
Threshold limit value
Relative risk reduction