Standardized Mean Differences: No So Standard After All.

ABSTRACT: Meta-analyses often use standardized mean differences (SMDs), such as Cohen's d and Hedges' g, to compare treatment effects. However, these SMDs are highly sensitive to the within-study sample variability used for their standardization, potentially distorting individual effect size estimates and compromising overall meta-analytic conclusions. This study introduces harmonized standardized mean differences (HSMDs), a novel sensitivity analysis framework designed to systematically evaluate and address such distortions. The HSMD harmonizes relative within-study variability across studies by employing the coefficient of variation (CV) to establish empirical benchmarks (e.g., CV quartiles). SMDs are then recalculated under these consistent variability assumptions. Applying this fra

SUBMITTER: Jung J 

PROVIDER: S-EPMC12358740 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature