### NNT (Number Needed to Treat)

#### 1. Description

NNT (Number Needed to Treat)[1][2] is derived from the probabilities of a favourable effect (benefits) for the treatment and comparator. NNT is sometimes known as NNTB (Number Needed to Treat to Benefit).[3] The difference between the two benefit probabilities, , gives the increase in certainty, . NNT is then calculated as the reciprocal of this difference, ; and can be interpreted as the number of patients that need to be treated (on average) for one favourable event (NNT) to be observed as a result of treatment.

#### 2. Evaluation

##### 2.1 Principle

- The calculation of NNT is transparent due to its apparent simplicity.

- The choice of input values used in the calculations need to be made more transparent e.g. in explicitly stating and justifying the source of data used.

- Input values must be rates/probabilities of the events of interest.

- Negative NNT is interpreted in the same way as an NNH.

- Negative NNT is interpreted in the same way as an NNH.

##### 2.2 Features

- NNT can only handle one event at a time; consequently benefit and risk are described separately.

- The final metric is easily understandable since it represents counts or the number of people.

##### 2.3 Visualisation

A potential visualisation to represent NNT is here

##### 2.4 Assessability and accessibility

- Although the required data are straightforward, the source of the rates/probabilities can be questionable due to the quality of data sources and may be biased.

- NNT are undefined when there is no difference between treatment and comparator group.

- The confidence intervals have also been criticised when the rates difference includes zero, leading to the confidence intervals of NNT to include infinity.

#### 3. References

[1] Holden WL, Juhaeri J, Dai W. Benefit-risk analysis: examples using quantitative methods. Pharmacoepidemiol Drug Saf 2003 Dec;12(8):693-7.[2] Laupacis A, Sackett DL, Roberts RS. An assessment of clinically useful measures of the consequences of treatment. N Engl J Med 1988 Jun 30;318(26):1728-33.

[3] Grieve R, Hutton J, Green C. Selecting methods for the prediction of future events in cost-effectiveness models: a decision-framework and example from the cardiovascular field. Health Policy 2003 Jun;64(3):311-24.