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Abstract
Collection of good-quality clinical data is expensive. It is important to choose methods for data analysis and presentation of results that allow clear assessment. For studies that compare rates of infection, or other adverse or beneficial outcomes, there are several ways in which the results can be summarized. The difference between the rates in two groups, say new treatment versus standard, can be used; this can be expressed as the absolute risk reduction (ARR). The ratio of rates, the relative risk (RR), is often used in epidemiological and survival analyses. Odds ratios (ORs) and log ORs are not so easy to understand but are useful in the analysis stage of research.
A statistic called the 'number needed to treat' (NNT) has been proposed, and is now included in some textbooks of Pharmaceutical Medicine and used in research articles and guidelines. The NNT is the inverse of the difference in rates and is usually expressed as a whole number. If the difference between the infection rates on two treatments is 17%, then 100 ÷ 17 = 6 is the NNT.
Other desirable aspects of a summary statistic were given by the authors who introduced NNT. It is important to be able to compare treatments, both in terms of benefits and harms and to identify patients who are most likely to benefit or to experience adverse effects. Subsequent claims were that NNT can be used to extrapolate research findings, is easy to calculate and allows the cost of treatment to be assessed. However, the claim that only NNT can provide a measure of benefit, harm and cost is simply false. A further issue, that RR and ORs combine two rates into one summary statistic, applies to NNT as well.
An important problem with NNT relates to its supposed main advantage: it is claimed that it provides a measure in terms of patients treated rather than in terms of probabilities. However, this is not true. An NNT gives the average number of patients, among whom, if they were treated with one therapy rather than another, exactly one patient will benefit. The NNT statistic is biased, and reliable confidence intervals cannot be provided. Furthermore, there is no simple value that indicates no difference between treatments. For meta-analysis, NNT cannot be used directly because simple arithmetic, such as addition, does not give correct results on the NNT scale. If the baseline rate, e.g. the mortality rate on standard treatment, is given, some transformations are required to be able to find the mortality rate on a new treatment using the NNT. If the risk reduction, the difference in rates, is given, the rate on new treatment is found by subtraction.
The major claim for NNT was that it is easy to understand, but this claim has been refuted by observational and experimental studies. It is best, for both theoretical and practical reasons, to use the baseline risk and the difference in success or event rates, the ARR, to present results. [PUBLICATION ABSTRACT]