PMID: 11914297Mar 27, 2002Paper

Can we individualize the 'number needed to treat'? An empirical study of summary effect measures in meta-analyses

International Journal of Epidemiology
Toshiaki A FurukawaLauren E Griffith

Abstract

Meta-analyses summarize the magnitude of treatment effect using a number of measures of association, including the odds ratio (OR), risk ratio (RR), risk difference (RD) and/or number needed to treat (NNT). In applying the results of a meta-analysis to individual patients, some textbooks of evidence-based medicine advocate individualizing NNT, based on the RR and the patient's expected event rate (PEER). This approach assumes constant RR but no empirical study to date has examined the validity of this assumption. We randomly selected a subset of meta-analyses from a recent issue of the Cochrane Library (1998, Issue 3). When a meta-analysis pooled more than three randomized controlled trials (RCT) to produce a summary measure for an outcome, we compared the OR, RR and RD of each RCT with the corresponding pooled OR, RR and RD from the meta-analysis of all the other RCT. Using the conventional P-value of 0.05, we calculated the percentage of comparisons in which there were no statistically significant differences in the estimates of OR, RR or RD, and refer to this percentage as the 'concordance rate'. For each effect measure, we made 1843 comparisons, extracted from 55 meta-analyses. The random effects model OR had the highest co...Continue Reading

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