### NNH (Number Needed to Harm)

#### 1. Description

NNH (Number Needed to Harm),[1][2] is defined similar to NNT but is based on the probabilities of unfavourable effects (risks) versus comparator. NNH is sometimes known as NNTH (Number Needed to Treat to Harm).[3] The difference between the two risk probabilities, , gives the increase in certainty, . NNH 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 unfavourable event (NNH) to be observed as a result of treatment.

#### 2. Evaluation

##### 2.1 Principle

- The calculation of NNH 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 NNH is interpreted in the same way as an NNT.

##### 2.2 Features

- NNH can only handle one event at a time; consequently multiple risks are described separately.

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

##### 2.3 Visualisation

- Visualisations similar to the one used in NNT may be used.

##### 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.

- NNH 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 NNH to include infinity.

This method was tested in the Natalizumab and Rimonabant case studies.

#### 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.