Assess heterogeneity in your meta-analysis with I², Cochran's Q, τ², and prediction intervals. Calculate the minimum number of studies needed to detect a given effect size.
Heterogeneity tab: Enter the effect size and standard error for each study in your meta-analysis. The tool calculates Cochran's Q, I², τ², and a 95% prediction interval using the DerSimonian-Laird method.
Power tab: Enter your expected effect size, significance level, and desired power to estimate the minimum number of studies needed.
Low heterogeneity. Studies are reasonably consistent. A fixed-effect model may be appropriate.
Moderate heterogeneity. Consider exploring sources via subgroup or meta-regression analyses.
High heterogeneity. Results should be interpreted with caution. Investigate sources before pooling.
I² describes the percentage of variability in effect estimates that is due to heterogeneity rather than sampling error. An I² of 0% means all variability is due to chance; 100% means all variability reflects true differences between studies. It was proposed by Higgins & Thompson (2002).
τ² is the between-study variance in a random-effects meta-analysis. Unlike I², τ² is on the scale of the effect size, making it useful for calculating prediction intervals. It's estimated using methods like DerSimonian-Laird or REML.
Use random-effects when you expect genuine variation between studies (different populations, interventions, settings). If I² > 0 or the Q test is significant, a random-effects model is generally more appropriate than fixed-effect.
Technically, you can pool 2+ studies. However, with fewer than 5 studies, estimates of between-study variance (τ²) are imprecise, and tests for heterogeneity have low power. Most methodologists recommend at least 5-10 studies for reliable random-effects results.
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