Letter to the editor: The unit-of-analysis error in meta-analysis: Why univariate tests cannot substitute for robust methodological synthesis

In brief

This letter addresses a critical methodological distinction in clinical research: why univariate statistical tests cannot replace comprehensive meta-analytic approaches. The author explains how meta-analysis uniquely handles between-study heterogeneity, weighting, and confidence intervals to produce robust aggregate effect estimates that isolated univariate comparisons cannot achieve.

• Meta-analysis requires integrated modeling of heterogeneity, weighting, and confidence intervals—not isolated statistical tests.
• Univariate tests assess single comparisons; they cannot replace methodological synthesis across heterogeneous studies.
• Robust meta-analytic inference depends on accounting for between-study variation and appropriate estimator selection.
• Clinical meaningfulness in meta-analysis emerges from systematic integration, not from aggregating univariate p-values.

Written by the GCMD Library team from the article.