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Simpson's paradox and calculation of number needed to treat from meta-analysis.

Cates, CJ (2002) Simpson's paradox and calculation of number needed to treat from meta-analysis. BMC Medical Research Methodology, 2 (1). ISSN 1471-2288
SGUL Authors: Cates, Christopher Joseph

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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.

Item Type: Article
Additional Information: Copyright: 2002 Cates; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.
Keywords: Bias (Epidemiology), Clinical Trials as Topic, Data Interpretation, Statistical, Humans, Meta-Analysis as Topic, Odds Ratio, Reproducibility of Results, Sample Size, Smoking Cessation, Statistics as Topic, General & Internal Medicine, 1117 Public Health And Health Services
SGUL Research Institute / Research Centre: Academic Structure > Population Health Research Institute (INPH)
Journal or Publication Title: BMC Medical Research Methodology
ISSN: 1471-2288
25 January 2002Published
PubMed ID: 11860604
Web of Science ID: 11860604
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