Abstract

See related article, pages 2433–2437. Numbers needed to treat (NNT) is a powerful tool that has been proposed to translate research findings of treatment effects to an intuitive figure for clinical settings or for policy-making purposes. It is a measure of clinical benefits that describes the number of individuals who would need to be treated to prevent the occurrence of 1 outcome event.1 NNT, most commonly, is simply calculated by the inverse of the absolute risk difference between 2 treatment options. However, important limitations should be considered when using NNT in communicating research findings. First, as with all relative or absolute treatment effect measures, causality must be assumed, which even in the setting of randomized controlled trials is not guaranteed if, for example, compliance or follow-up rates differ according to treatment groups or blinding of treatments cannot be achieved. Second, NNT is specific to a comparison of treatment effects in 1 study population. Thus, it should be considered specific to a particular comparison and not necessarily to a particular therapy.2 Strictly speaking, NNT only applies to patients of a specific trial and the application of this NNT to any other setting is an extrapolation, which may or may not be valid. Third, the NNT will vary across groups with different baseline …

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