INTRODUCTION

Heterogeneity denotes the variation in effect sizes among studies. Assessing heterogeneity is a critical step in meta-analysis as it influences model choice and interpretation of results (Kepes et al., 2023).

Understanding and addressing heterogeneity in meta-analysis is essential for drawing accurate and generalizable conclusions. One key method for exploring the sources of heterogeneity is moderator analysis, which involves identifying variables that may influence the effect sizes observed in different studies. This approach explains why studies might yield different results and under what conditions specific effects are stronger or weaker.

Heterogeneity in meta-analysis can manifest in three ways: clinical heterogeneity, methodological heterogeneity, and statistical heterogeneity. Clinical heterogeneity involves variability in participants, interventions, and outcomes; methodological heterogeneity includes differences in study design, quality, and execution; and statistical heterogeneity refers to variability in effect sizes that cannot be attributed to chance alone.