A well-executed meta-analysis provides high statistical power by mathematically synthesizing evidence across multiple independent studies. Choosing appropriate statistical models is critical for valid inferences.
Fixed-Effect vs. Random-Effects Models
Use fixed-effect models only when assuming a single true effect size across identical populations. In biomedical and social research, random-effects models (DerSimonian-Laird or REML) are almost always preferred due to inherent between-study variance.
Evaluating Study Heterogeneity
- I² Statistic: Quantifies the percentage of total variation across studies attributable to heterogeneity (25% = low, 50% = moderate, 75% = substantial).
- Subgroup & Meta-Regression Analysis: Used to explore potential moderators explaining observed variance.
