Heterogeneity and Prediction Intervals in Meta-Analysis

This chapter guides understanding and interpreting heterogeneity [within- and between-study variation] in meta-analyses, crucial for applying findings. We explain why heterogeneity matters and detail four key metrics: Cochran's Q, I2, τ2, and H statistic. For each, the chapter will clarify meaning, strengths, limitations, and interpretation, stressing they offer complementary views and shouldn't be used in isolation. Prediction intervals are introduced as a tool to translate statistical heterogeneity into clinically meaningful forecasts of future study effects. We provide guidance on addressing high heterogeneity via subgroup analyses, meta-regression, and outlier/sensitivity analyses, emphasizing transparent reporting [PRISMA 2020]. This practical guide promotes a nuanced understanding of heterogeneity's statistical and clinical implications in meta-analysis, moving beyond simplistic metric interpretation.