What is statistical heterogeneity, what is its effect on meta-analysis, and how should it be evaluated?
Statistical heterogeneity is “variation between trials in the underlying treatment effects being evaluated” (Higgins, Thompson, Deeks, et al., 2002) and is a consequence of clinical heterogeneity (e.g., differences among patients, interventions, outcomes) and methodological heterogeneity (e.g., differences in study designs, sources of bias). Statistical heterogeneity among studies combined in meta-analysis may be detected if “variation in the results of the studies is above that compatible with chance alone” (Higgins, Thompson, Deeks, et al., 2002). The traditional test statistic (Cochran’s Q) for evaluating heterogeneity has low power when studies are few, and may have excessive power when studies are many and large (Higgins, Thompson, Deeks, et al., 2003). A more recently-introduced test statistic, called I2, “describes the percentage of total variation across studies that is due to heterogeneity” (Higgins, Thompson, Deeks, et al., 2003). An I2 value of 0% indicates no observed heter
