Résumé :
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[BDSP. Notice produite par INIST-CNRS qosoER0x. Diffusion soumise à autorisation]. Background : Several approaches are available for evaluating heterogeneity in meta-analysis. Sensitivity analyses are often used, but these are often implemented in various non-standardized ways. Methods : We developed and implemented sequential and combinatorial algorithms that evaluate the change in between-study heterogeneity as one or more studies are excluded from the calculations. The algorithms exclude studies aiming to achieve either the maximum or the minimum final I2 below a desired pre-set threshold. We applied these algorithms in databases of meta-analyses of binary outcome and>=4 studies from Cochrane Database of Systematic Reviews (Issue 4,2005, n=1011) and meta-analyses of genetic associations (n=50). Two I2 thresholds were used (50% and 25%). Results : Both algorithms have succeeded in achieving the pre-specified final I2 thresholds. Differences in the number of excluded studies varied from 0% to 6% depending on the database and the heterogeneity threshold, while it was common to exclude different specific studies. Among meta-analyses with initial I2>50%, in the large majority [19 (90.5%) and 208 (85.9%) in genetic and Cochrane meta-analyses, respectively] exclusion of one or two studies sufficed to decrease I225%, in most cases [16 (57.1%) and 382 (81.3%), respectively) exclusion of one or two studies sufficed to decrease heterogeneity even
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