Meta-analysis, Simpson's paradox, and the number needed to treat
- PMID: 11860606
- PMCID: PMC65634
- DOI: 10.1186/1471-2288-2-3
Meta-analysis, Simpson's paradox, and the number needed to treat
Abstract
Background: There is debate concerning methods for calculating numbers needed to treat (NNT) from results of systematic reviews.
Methods: We investigate the susceptibility to bias for alternative methods for calculating NNTs through illustrative examples and mathematical theory.
Results: Two competing methods have been recommended: one method involves calculating the NNT from meta-analytical estimates, the other by treating the data as if it all arose from a single trial. The 'treat-as-one-trial' method was found to be susceptible to bias when there were imbalances between groups within one or more trials in the meta-analysis (Simpson's paradox). Calculation of NNTs from meta-analytical estimates is not prone to the same bias. The method of calculating the NNT from a meta-analysis depends on the treatment effect used. When relative measures of treatment effect are used the estimates of NNTs can be tailored to the level of baseline risk.
Conclusions: The treat-as-one-trial method of calculating numbers needed to treat should not be used as it is prone to bias. Analysts should always report the method they use to compute estimates to enable readers to judge whether it is appropriate.
Comment on
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Simpson's paradox and calculation of number needed to treat from meta-analysis.BMC Med Res Methodol. 2002;2:1. doi: 10.1186/1471-2288-2-1. Epub 2002 Jan 25. BMC Med Res Methodol. 2002. PMID: 11860604 Free PMC article.
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- Deeks JJ, Altman DG. Effect measures for meta-analysis of trials with binary outcomes. In: Egger M, Davey Smith G, Altman DG, editor. Systematic Reviews in Health Care. Meta-analysis in Context. London: BMJ Books; 2001. pp. 313–335.
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- Deeks JJ. Issues in selection of a summary statistic for meta-analysis of clinical trials with binary data. Stat Med. - PubMed
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