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Langan, Dean; Higgins, Julian P. T.; Simmonds, Mark – Research Synthesis Methods, 2017
Random-effects meta-analysis methods include an estimate of between-study heterogeneity variance. We present a systematic review of simulation studies comparing the performance of different estimation methods for this parameter. We summarise the performance of methods in relation to estimation of heterogeneity and of the overall effect estimate,…
Descriptors: Meta Analysis, Simulation, Comparative Analysis, Intervals
Borenstein, Michael; Higgins, Julian P. T.; Hedges, Larry V.; Rothstein, Hannah R. – Research Synthesis Methods, 2017
When we speak about heterogeneity in a meta-analysis, our intent is usually to understand the substantive implications of the heterogeneity. If an intervention yields a mean effect size of 50 points, we want to know if the effect size in different populations varies from 40 to 60, or from 10 to 90, because this speaks to the potential utility of…
Descriptors: Meta Analysis, Effect Size, Intervention, Prediction
Mawdsley, David; Higgins, Julian P. T.; Sutton, Alex J.; Abrams, Keith R. – Research Synthesis Methods, 2017
In meta-analysis, the random-effects model is often used to account for heterogeneity. The model assumes that heterogeneity has an additive effect on the variance of effect sizes. An alternative model, which assumes multiplicative heterogeneity, has been little used in the medical statistics community, but is widely used by particle physicists. In…
Descriptors: Databases, Meta Analysis, Goodness of Fit, Effect Size

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