Why meta-analysis can mislead: when the highest level of evidence fails in evidence-based medicine
DOI:
https://doi.org/10.17267/2675-021Xevidence.2026.e6898Keywords:
Evidence-Based Medicine, Meta-Analysis, Bias, Reproducibility of Results, Clinical Decision-MakingAbstract
CONTEXT: Meta-analysis is widely positioned at the apex of the evidence hierarchy in evidence-based medicine (EBM), often serving as the foundation for clinical guidelines, policy decisions, and therapeutic recommendations. Their quantitative nature conveys an appearance of precision and objectivity that reinforces their authority in clinical reasoning. However, this perceived robustness may obscure important methodological and epistemological limitations. CONCEPTUALIZATION: This article critically examines the conditions under which meta-analysis may produce misleading inferences. We argue that the aggregation of evidence does not inherently generate validity, particularly in the presence of heterogeneity, publication bias, and variable study quality. By exploring structural limitations, including analytical flexibility, amplification of bias, and the misinterpretation of statistical significance, we demonstrate how meta-analysis can create an illusion of certainty. Rather than resolving uncertainty, they may repack it into quantitatively precise but conceptually fragile estimates. We propose that meta-analysis should be interpreted as conditional and context-dependent constructs, whose validity depends on rigorous methodological scrutiny and epistemological awareness. Reframing their role within EBM is essential to prevent overreliance and to promote more critical, responsible clinical decision-making.
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Copyright (c) 2026 Guilherme Rodrigues Oliveira, Isabela Martins Vecchi
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The authors retain copyrights, transferring to the Journal of Evidence-Based Healthcare only the right of first publication. This work is licensed under a Creative Commons Attribution 4.0 International License.
