How to Interpret Heterogeneity in Meta-Analysis: A Structured Guide for Clinicians and Researchers

Heterogeneity is central to the credibility, transportability, and clinical interpretation of meta-analytic evidence, yet its assessment is frequently reduced to isolated or misinterpreted statistics. This tutorial provides a clinically oriented framework for interpreting heterogeneity across therapeutic, diagnostic, and prognostic evidence synthesis. It distinguishes sampling error from genuine between-study variability and explains why Q, I2, τ2, and prediction intervals should be interpreted as complementary rather than interchangeable measures. Rather than relying on I2 alone, meaningful interpretation requires assessing both the relative and absolute scale of between-study variability and its implications for future clinical settings. The tutorial also addresses estimator dependence, uncertainty in τ2, the limitations of different confidence-interval approaches in random-effects models, and the clinical interpretation of wide prediction intervals, including links with certainty-of-evidence judgments. Diagnostic test accuracy meta-analysis is discussed as a bivariate problem that requires hierarchical models, variance components, correlation parameters, and prediction regions, rather than univariate I2 summaries. Prognostic reviews are framed around case mix, baseline risk, discrimination, calibration, and transportability. Worked simulated examples, formal expressions, and practical algorithms are used to support decision-making about when pooling is reasonable, when it should be qualified, and when it should be avoided. Accurate interpretation of heterogeneity requires domain-appropriate modeling, transparent reporting, and clinical judgment rather than reliance on any single statistic.