What should accompany a meta-analysis to ensure robust findings?

To ensure robust findings in a meta-analysis, conducting a test of statistical heterogeneity is essential. Heterogeneity refers to the variability in study outcomes that can arise from differences in study population, interventions, and methodologies employed across the included studies. By assessing heterogeneity, researchers can determine whether the results of the studies can be meaningfully combined. This assessment helps identify whether the observed variations in effects are statistically significant or if they can be attributed to random chance.

When significant heterogeneity is detected, it suggests that the studies may not be measuring the same underlying effect, which can compromise the validity of the overall conclusions drawn from the meta-analysis. Therefore, performing a test for statistical heterogeneity, such as I² statistics or Q-tests, is crucial for interpreting the meta-analysis results accurately, guiding whether a fixed or random-effects model should be utilized, and informing the appropriateness of pooling the data from different studies. This step enhances the reliability of the meta-analysis findings, ensuring that conclusions reflect true effects rather than artifacts of study variation.