The NNT is the average number of patients who need to be treated to prevent one additional bad outcome.
The modern approach, based on counterfactual conditionals, relaxes this assumption and yields bounds on NNT.
A combined measure, the number needed to treat for an additional beneficial or harmful outcome (NNTB/H), is also used.
If a clinical endpoint is devastating enough (e.g. death, heart attack), drugs with a high NNT may still be indicated in particular situations.
[7] There are several important problems with the NNT, involving bias and lack of reliable confidence intervals, as well as difficulties in excluding the possibility of no difference between two treatments or groups.
[8] NNT may vary substantially over time,[9][10] and hence convey different information as a function of the specific time-point of its calculation.
Snapinn and Jiang[11] showed examples where the information conveyed by the NNT may be incomplete or even contradictory compared to the traditional statistics of interest in survival analysis.
In this example, it is important to understand that every participant has the condition being treated, so there are only "diseased" patients who received the treatment or did not.
This is typically a type of study that would occur only if both the control and the tested treatment carried significant risks of serious harm, or if the treatment was unethical for a healthy participant (for example, chemotherapy drugs or a new method of appendectomy - surgical removal of the appendix).
Prospective studies produce much higher quality evidence, but are much more difficult and time-consuming to perform.
[citation needed] In the table below: ASCOT-LLA manufacturer-sponsored study addressed the benefit of atorvastatin 10 mg (a cholesterol-lowering drug) in patients with hypertension (high blood pressure) but no previous cardiovascular disease (primary prevention).
[16] The above calculations for NNT are valid under monotonicity, where treatment can't have a negative effect on any individual.
However, in the case where the treatment may benefit some individuals and harm others, the NNT as defined above cannot be estimated from a Randomized Controlled Trial (RCT) alone.
The modern approach defines NNT literally, as the number of patients one needs to treat (on the average) before saving one.
[19][20] Mueller and Pearl provide a conceptual interpretation for this phenomenon and illustrate its impact on both individual and policy-makers decisions.