The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Simpson's paradox and calculation of number needed to treat from meta-analysis
Open accessAbstract
BACKGROUND: Calculation of numbers needed to treat (NNT) is more complex from meta-analysis than from single trials. Treating the data as if it all came from one trial may lead to misleading results when the trial arms are imbalanced. DISCUSSION: An example is shown from a published Cochrane review in which the benefit of nursing intervention for smoking cessation is shown by formal meta-analysis of the individual trial results. However if these patients were added together as if they all came from one trial the direction of the effect appears to be reversed (due to Simpson's paradox). Whilst NNT from meta-analysis can be calculated from pooled Risk Differences, this is unlikely to be a stable method unless the event rates in the control groups are very similar. Since in practice event rates vary considerably, the use a relative measure, such as Odds Ratio or Relative Risk is advocated. These can be applied to different levels of baseline risk to generate a risk specific NNT for the treatment. SUMMARY: The method used to calculate NNT from meta-analysis should be clearly stated, and adding the patients from separate trials as if they all came from one trial should be avoided.
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“On the other hand, unlike risk ratios which “may be fairly independent of the patients’ baseline risk status,” ARR and NNT measures can be pooled together in meta-analyses if “the event rates in the control groups are very similar” [27].”
From Relative risk reduction: Misinformative measure in clinical trials and COVID-19 vaccine efficacy (2022) · cites this paper as [27]
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Cites
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COVID-19 and vaccine hesitancy: A longitudinal study
Fridman, A., Gershon, R., & Gneezy, A. (2020) · PLoS ONE · doi:10.1371/journal.pone.0250123 · Cited by 697
Singh, J. P., Grann, M., & Fazel, S. (2010) · Clinical Psychology Review · doi:10.1016/j.cpr.2010.11.009 · Cited by 666
Psychosocial interventions for supporting women to stop smoking in pregnancy
Chamberlain, C., O'Mara-Eves, A., Porter, J., Coleman, T., Perlen, S. M., Thomas, J., & McKenzie, J. E. (2017) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd001055.pub5 · Cited by 650
Recent developments in meta‐analysis
Sutton, A. J., & Higgins, J. P. T. (2007) · Statistics in Medicine · doi:10.1002/sim.2934 · Cited by 601
Long-term use of thiazolidinediones and fractures in type 2 diabetes: a meta-analysis
Loke, Y. K., Singh, S., & Furberg, C. D. (2008) · Canadian Medical Association Journal · doi:10.1503/cmaj.080486 · Cited by 553
Metadata
- Authors
- Cates, C. J.
- Corresponding author
- Cates, Christopher J
- ORCID
- Cates, Christopher J
- Venue
- BMC Medical Research Methodology
- Published
- 2002-01-25
- Volume
- 2
- Issue
- 1
- Pages
- 1-1
- ISSN
- 1471-2288 (ISSN-L)
- PMC
- PMC65632
- OpenAlex
- W1576173198
- Cited by
- 251
- References
- 17
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 7 · 2023: 3 · 2022: 3 · 2021: 20 · 2020: 13 · 2019: 14 · 2018: 16 · 2017: 22 · 2016: 23 · 2015: 8 · 2014: 13 · 2013: 13 · 2012: 10
- FWCI
- 10.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Medical Research Methodology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, other-oa)
- PubMed (publishedVersion)
- St George's Online Research Archive (St George's University of London) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- Simpson's paradox, number needed to treat, meta-analysis, risk difference, odds ratio, relative risk, baseline risk, smoking cessation, nursing interventions
- MeSH terms
- Clinical Trials as Topic, Data Interpretation, Statistical, Humans, Statistics as Topic, Meta-Analysis as Topic, Reproducibility of Results, Bias, Odds Ratio, Smoking Cessation, Sample Size
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