Bayesian meta-analysis in the 21st century: Fad or future of evidence synthesis?

Meta-analyses are pivotal in evidence-based medicine. They synthesise findings from multiple studies to inform clinical practice. Traditionally, frequentist statistical methods have dominated this field. However, growing recognition of their limitations in interpreting uncertainty and integrating prior knowledge has led to increased interest in Bayesian methods. We compared the Bayesian and frequentist approaches to meta-analysis, assessed their respective strengths and limitations, and evaluated how each technique influences the interpretation of clinical trial data, particularly when evidence is ambiguous. A narrative review was conducted to understand and compare key aspects of frequentist and Bayesian statistics. Real-world clinical examples were used to illustrate the differences between how each statistical paradigm interprets the same dataset. Frequentist methods were assessed based on null hypothesis testing and P values, while Bayesian methods were evaluated for their use of prior distributions and posterior probabilities. We demonstrated how statistical significance of the obtained result may differ based on whether a frequentist or Bayesian statistical analysis was employed. In most cases, Bayesian methods enabled increased intuitiveness and flexibility of interpretations by incorporating uncertainty and pre-existing evidence. While frequentist methods remain standard for simpler univariate analyses, the Bayesian approach offers a more holistic framework for complex meta-analyses, as it aligns more closely with practical clinical decision-making. This places Bayesian inference a valuable tool in the future of evidence synthesis.

Abstract licence: CC BY-NC-SA

21 more references, whose wording the paper’s licence keeps private.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.