Group exposed to a<unk> ment (left) has reduced risk of an adverse outcome (grey) compared to the unexposed group (right). 4 individuals need to be<unk> ed to prevent 1 adverse outcome <unk>  = 4). The<unk>  <unk>  is an epidemiological measure used in communicating the effectiveness of a health-care intervention, typically a<unk> ment with medication. The<unk>  is the average<unk>  of patients who need to be<unk> ed to prevent one additional bad outcome (e.g. the<unk>  of patients that need to be<unk> ed for one of them to benefit compared with a control in a clinical trial). It is defined as the inverse of the absolute risk reduction, and computed as , where is the incidence in the<unk> ed (exposed) group, and is the incidence in the control (unexposed) group. This calculation implicitly assumes monotonicity, that is, no individual can be harmed by<unk> ment. The modern approach, based on counterfactual conditionals, relaxes this assumption and yields bounds on<unk> . A type of effect size, the<unk>  was described in 1988 by McMaster University's Laupacis, Sackett and Roberts. The ideal<unk>  is 1, where everyone improves with<unk> ment and no one improves with control. A higher<unk>  indicates that<unk> ment is less effective.<unk>  is similar to<unk>  needed to harm (NNH), where<unk>  usually refers to a therapeutic intervention and NNH to a detrimental effect or risk factor.
Number needed to treat
Number needed to harm
Risk of mortality
Relative risk reduction
Minimal important difference
Indicative limit value
N of 1 trial
Sensitivity and specificity