The number needed to treat: a clinically useful measure of treatment effectBMJ 1995; 310 doi: https://doi.org/10.1136/bmj.310.6977.452 (Published 18 February 1995) Cite this as: BMJ 1995;310:452
- Richard J Cook, assistant professora,
- David L Sackett, professor of clinical epidemiologyb
- a Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada N2L 3G1
- b Nuffield Department of Clinical Medicine (Level 5), John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU
- Correspondence to: Professor Cook.
The relative benefit of an active treatment over a control is usually expressed as the relative risk, the relative risk reduction, or the odds ratio. These measures are used extensively in both clinical and epidemiological investigations. For clinical decision making, however, it is more meaningful to use the measure “number needed to treat.” This measure is calculated on the inverse of the absolute risk reduction. It has the advantage that it conveys both statistical and clinical significance to the doctor. Furthermore, it can be used to extrapolate published findings to a patient at an arbitrary specified baseline risk when the relative risk reduction associated with treatment is constant for all levels of risk.
More emphasis is now being put on effective use of biomedical literature to guide clinical treatment. As a result accessing, critically appraising, and incorporating the results of clinical investigations into clinical practice are becoming higher priorities for doctors and medical students.1
A pivotal step in translating clinical research into practice is the summarisation of data from randomised trials in terms of measures of effect that can be readily appreciated by doctors and other carers. Various measures of the effect of treatment are used in analysing results. Each measure has its own interpretation and statistical properties that make it suitable for some applications but perhaps not for others. We describe here a new measure referred to as number needed to treat2 and a simple method of adopting this approach to individual patients at different levels of risk.
Measures of treatment effect
Consider a parallel group study in which patients are randomised to either an active treatment or a placebo control arm, are followed for a fixed amount of time, and are observed to experience a binary response to treatment (event/no event). We assume here that the events are adverse, and …