Number needed to treat (NNT) in clinical literature: an appraisal

Abstract

BackgroundThe number needed to treat (NNT) is an absolute effect measure that has been used to assess beneficial and harmful effects of medical interventions. Several methods can be used to calculate NNTs, and they should be applied depending on the different study characteristics, such as the design and type of variable used to measure outcomes. Whether or not the most recommended methods have been applied to calculate NNTs in studies published in the medical literature is yet to be determined. The aim of this study is to assess whether the methods used to calculate NNTs in studies published in medical journals are in line with basic methodological recommendations.MethodsThe top 25 high-impact factor journals in the “General and/or Internal Medicine” category were screened to identify studies assessing pharmacological interventions and reporting NNTs. Studies were categorized according to their design and the type of variables. NNTs were assessed for completeness (baseline risk, time horizon, and confidence intervals [CIs]). The methods used for calculating NNTs in selected studies were compared to basic methodological recommendations published in the literature. Data were analyzed using descriptive statistics.ResultsThe search returned 138 citations, of which 51 were selected. Most were meta-analyses (n = 23, 45.1%), followed by clinical trials (n = 17, 33.3%), cohort (n = 9, 17.6%), and case–control studies (n = 2, 3.9%). Binary variables were more common (n = 41, 80.4%) than time-to-event (n = 10, 19.6%) outcomes. Twenty-six studies (51.0%) reported only NNT to benefit (NNTB), 14 (27.5%) reported both NNTB and NNT to harm (NNTH), and 11 (21.6%) reported only NNTH. Baseline risk (n = 37, 72.5%), time horizon (n = 38, 74.5%), and CI (n = 32, 62.7%) for NNTs were not always reported. Basic methodological recommendations to calculate NNTs were not followed in 15 studies (29.4%). The proportion of studies applying non-recommended methods was particularly high for meta-analyses (n = 13, 56.5%).ConclusionsA considerable proportion of studies, particularly meta-analyses, applied methods that are not in line with basic methodological recommendations. Despite their usefulness in assisting clinical decisions, NNTs are uninterpretable if incompletely reported, and they may be misleading if calculating methods are inadequate to study designs and variables under evaluation. Further research is needed to confirm the present findings.Electronic supplementary materialThe online version of this article (doi:10.1186/s12916-017-0875-8) contains supplementary material, which is available to authorized users.

Improved Survival With Higher Pre-diagnosis Cardiorespiratory Fitness in Men Who Developed Digestive System Cancers: A Prospective Pilot Study

“…Exposure impact number (EIN) was calculated as EIN=1/[(RR-1)*PNE]+RR/[(RR-1)*(1-PNE)] where RR=fully adjusted relative risk from Cox hazard model accounting for competing events and PNE=event rate in non-exposed population (24)(25)(26)(27). EIN is the corresponding epidemiological measure of a number needed to treat (NNT), an analysis commonly used in randomized controlled trials.…”

“…EIN and NNT permit quantification of effort needed to be taken for preventing one event, thus providing an objective comparison of cost-effectiveness with other treatments or interventions. In general, the lower EINs and NNTs suggest a more cost-effective intervention for the prevention of an adverse outcome (24)(25)(26)(27). Data report and presentation followed the "Strengthening the Reporting of Observational Studies in Epidemiology" (STROBE) guidelines (28).…”

The Risk of Prior Opioid Exposure on Future Opioid Use and Comorbidities in Individuals With Non-Acute Musculoskeletal Knee Pain

“… 22 In contrast, absolute risk can provide a better representation of the clinical impact of an exposure. 23 The numbers needed to harm is the number of patients exposed to the event of interest (eg, opioids) needed before the outcome of interest is present (ie, future opioid use), 24 and provides a clinically relevant value that is simple for clinicians to use. We hypothesized that prior opioid use and prior comorbid diagnoses would be significant risk factors for future opioid use.…”

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