The Number Needed to Treat: 25 Years of Trials and Tribulations in Clinical Research

• Main Author: Samy Suissa
• Format: Article
• Language: English
• Published: Rambam Health Care Campus 2015-07-01
• Series: Rambam Maimonides Medical Journal
• Subjects: Biostatistics; effect measures; epidemiologic methods; observational studies; randomized controlled trials; treatment impact
• Online Access: http://rmmj.org.il/Pages/ArticleHTM.aspx?manuId=508

The number needed to treat (NNT) is a simple measure of a treatment’s impact, increasingly reported in randomized trials and observational studies, but often incorrectly calculated in studies involving varying follow-up times. We discuss the NNT in these contexts and illustrate the concept using several published studies. While the computation of the NNT is founded on the cumulative incidence of the outcome, several published studies use simple proportions that do not account for varying follow-up times, or use incidence rates per person-time. We show how these approaches can lead to erroneous values of the NNT and misleading interpretations. For example, a trial of 3,845 very elderly hypertensives randomized to a diuretic or placebo reported a NNT of 94 treated for 2 years to prevent one stroke, though the correct approach results in a NNT of 63. Also, meta-analyses involving trials of differing lengths often report a single NNT, such as the meta-analysis of 22 trials of the anticholinergic tiotropium in chronic obstructive pulmonary disease that reported a NNT of 16 patients “over one year,” even if the trials varied in duration from 3 to 48 months, with the actual NNTs varying widely from 15 to 250. Finally, we describe the value of the NNT in assessing benefit–risk, such as low-dose aspirin use in secondary prevention of mortality assessed against the risk of gastrointestinal bleeding. As the “number needed to treat” becomes increasingly used in the comparative effectiveness and safety of therapies, its accurate estimation and interpretation become crucial to avoid distorting clinical, economic, and public health decisions.