Subgroup analysis and meta-regression are the two primary methods for investigating why effect sizes vary across studies in a meta-analysis. When your I-squared value is high, indicating substantial explore heterogeneity beyond what chance alone would explain, these methods help identify which study-level characteristics are associated with larger or smaller effects. Understanding when and how to use each method is essential for producing informative, transparent meta-analyses that go beyond a single pooled estimate.
Both methods address the same fundamental question: do effect sizes differ depending on specific study characteristics? But they differ in how they model this relationship. Subgroup analysis is simpler, dividing studies into discrete groups and comparing pooled estimates. Meta-regression is more flexible, using weighted regression to model the relationship between covariates and effect sizes. The choice between them depends on the nature of your moderator variables, the number of included studies, and whether your investigation is prespecified or exploratory.
When to Investigate Heterogeneity
Before conducting subgroup analysis or meta-regression, confirm that meaningful heterogeneity exists. The Cochrane Handbook recommends investigating heterogeneity when:
- I-squared exceeds 50 percent, suggesting moderate to substantial heterogeneity
- The Q-test is statistically significant, indicating that between-study variation exceeds what would be expected by chance
- Clinical or methodological diversity among included studies suggests that effect sizes may legitimately differ
- The prediction interval around the pooled estimate is wide, indicating that the true effect in a new study could differ substantially from the average
If heterogeneity is low (I-squared below 30%) and studies are clinically similar, subgroup analysis and meta-regression add little value and may produce spurious findings through multiple testing.
Subgroup Analysis: Methodology
How It Works
Subgroup analysis divides included studies into groups based on a categorical study-level characteristic and then calculates separate pooled effect estimates for each group. The key output is the test for subgroup differences (also called the interaction test or between-group Q-test), which evaluates whether the pooled estimates differ significantly between groups.
Example: A meta-analysis of exercise interventions for depression includes studies from high-income and low-income countries. Subgroup analysis pools the effect estimate separately for each country group and tests whether the pooled effects are statistically different.
Step-by-Step Process
- Prespecify your subgroups in the protocol. Limit to 3-5 subgroups with strong clinical or theoretical rationale
- Classify each study into the appropriate subgroup based on the moderator variable
- Pool effect sizes within each subgroup using the same meta-analytical model as your main analysis
- Conduct the test for subgroup differences to determine whether the between-group variation is statistically significant
- Present results with separate explore forest plots or a single forest plot with subgroup sections
- Interpret with caution, especially if the analysis was not prespecified
Interpreting the Test for Subgroup Differences
The correct way to evaluate subgroup differences is the interaction test, which directly compares the pooled estimates between groups. A common mistake is comparing whether individual subgroup estimates are statistically significant. This approach is flawed because a significant estimate in one subgroup and a non-significant estimate in another does not mean the effects are different; the confidence intervals may overlap substantially.
