↓ Skip to main content

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

Overview of attention for article published in BMC Medicine, June 2017
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)

Mentioned by

147 tweeters
5 Facebook pages


56 Dimensions

Readers on

96 Mendeley
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Number needed to treat (NNT) in clinical literature: an appraisal
Published in
BMC Medicine, June 2017
DOI 10.1186/s12916-017-0875-8
Pubmed ID

Diogo Mendes, Carlos Alves, Francisco Batel-Marques


The 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. The 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. The 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%). A 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.

Twitter Demographics

The data shown below were collected from the profiles of 147 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 96 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 15%
Researcher 11 11%
Student > Ph. D. Student 10 10%
Student > Doctoral Student 9 9%
Professor > Associate Professor 8 8%
Other 31 32%
Unknown 13 14%
Readers by discipline Count As %
Medicine and Dentistry 42 44%
Nursing and Health Professions 16 17%
Pharmacology, Toxicology and Pharmaceutical Science 4 4%
Neuroscience 4 4%
Psychology 4 4%
Other 9 9%
Unknown 17 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 77. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 May 2020.
All research outputs
of 17,899,822 outputs
Outputs from BMC Medicine
of 2,748 outputs
Outputs of similar age
of 280,106 outputs
Outputs of similar age from BMC Medicine
of 1 outputs
Altmetric has tracked 17,899,822 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,748 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 39.3. This one has done well, scoring higher than 89% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 280,106 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them