Confidence Intervals for Heterogeneity Measures in Meta-analysis

Abstract

Two methods of quantifying heterogeneity between studies in meta-analysis were studied. One method quantified the proportion of the total variance of the effect estimate due to variation between studies (RI), and the other calibrated the variance between studies to the size of the effect itself through a between-study coefficient of variation (CVB). Bootstrap and asymptotic confidence intervals for RI and CVB were derived and evaluated in an extensive simulation study that covered a wide range of scenarios likely to be encountered in practice. The best performance was given by asymptotic Wald confidence intervals developed for RI and CVB. The use of these heterogeneity measures together with their confidence intervals was illustrated in 5 typical meta-analyses. A new user-friendly SAS macro (SAS Institute, Inc., Cary, North Carolina) is provided to implement these methods for routine use and can be downloaded at the last author's website.