Table 1.

General recommendations for meta-analysis of clinical studies.

1. Include published and unpublished studies on the basis of inclusion/exclusion criteria (e.g., designs, measures, sample characteristics). Ideally, pre-register your meta-analysis on an accessible server (1) (e.g., PROSPERO database, Open Science Framework)

2. Systematically run heterogeneity tests (Q statistic, the variance between studies (τ2), and the relationship between the real heterogeneity and the total variation observed, I2). Some depend on the number of participants (Q) whereas other depends on the metric scale (τ2) so it is crucial to compare them to estimate true heterogeneity (2, 3)

3. In case of substantial heterogeneity (i.e., I2 > 75%), create homogenous subgroups based on theoretical or methodological justifications (4)

4. Estimate publication bias using funnel plots and inferential tests (i.e., Begg's/Egger's tests). In case of publication bias, run additional analysis comparing the main results with/without these studies (2, 3)

5. Evaluate p-hacking using p-curve. If H0 is true (no effect), the p-distribution must be uniform but right-skewed if there is an effect. In case of signs of p-hacking, exclude those studies and run again the analysis to compare the results (5)

6. Conduct separate analyses for observational, quasi-experimental, and experimental studies and evaluate the risk of bias for each study (6).