An additional test, due to Breslow and Day (1980), is provided with the odds ratio meta-analysis. It is arguably not possible to examine the null hypothesis that all studies are evaluating the same effect, by considering the only the summary data from the studies: The heterogeneity test results should be considered alongside a qualitative assessment of the combinability of studies in a systematic review.
The I² statistic describes the percentage of variation across studies that is due to heterogeneity rather than chance ( Higgins and Thompson, 2002 Higgins et al., 2003). I² is an intuitive and simple expression of the inconsistency of studies’ results.
Unlike Q it does not inherently depend upon the number of studies considered. A confidence interval for I² is constructed using either i) the iterative non-central chi-squared distribution method of Hedges and Piggott (2001) or ii) the test-based method of Higgins and Thompson (2002). The L'Abbé plot can be used to explore the inconsistency of studies visually.Ĭhoosing between fixed and random effects models The non-central chi-square method is currently the method of choice (Higgins, personal communication, 2006) – it is computed if the 'exact' option is selected. If there is very little variation between trials then I² will be low and a fixed effects model might be appropriate.
