1️⃣ Between-Study Heterogeneity and Model Selection
One of the most critical challenges in meta-analysis is managing between-study heterogeneity. Clinical diversity (population differences), methodological diversity (study design variations), and statistical heterogeneity (variation in effect sizes) can significantly influence pooled estimates. Choosing between fixed-effect and random-effects models is not merely technical—it changes the interpretation of the summary effect. In high heterogeneity settings (I² > 50%), a random-effects model accounts for variability but also widens confidence intervals, affecting precision and inference.
Example: Suppose five RCTs evaluate colchicine in acute coronary syndrome, but differ in follow-up duration and dosage. A fixed-effect model may overestimate precision, while a random-effects model better reflects real-world variability in treatment effect.
2️⃣ Publication Bias and Small-Study Effects
Publication bias remains a serious threat to the validity of meta-analytic findings. Studies with statistically significant results are more likely to be published, which inflates pooled effect estimates. Funnel plot asymmetry, Egger’s regression test, and trim-and-fill methods are commonly used to assess small-study effects. However, asymmetry does not always imply bias—it may reflect true heterogeneity or methodological differences. Therefore, interpretation requires both statistical testing and clinical judgment.
Example: If smaller trials show exaggerated benefits of a drug while larger trials show modest or null effects, the pooled odds ratio may appear significant due to small-study effects rather than true efficacy.
3️⃣ Meta-Regression and Effect Modification
Meta-regression extends conventional meta-analysis by exploring whether study-level covariates explain heterogeneity. Variables such as mean age, baseline risk, or intervention dosage can be incorporated into a regression framework. However, meta-regression operates at the study level, not the patient level, which introduces ecological bias and limits causal interpretation. It should therefore be hypothesis-generating rather than confirmatory.
Example: In a meta-analysis of heart failure therapies, meta-regression may reveal that treatment effect increases with higher baseline BNP levels across studies. However, this does not confirm that individual patients with high BNP derive greater benefit.
