Bayesian methods seem a natural choice for combining sources of evidence in meta-analyses. However, in practice, their sensitivity to the choice of prior distribution is much less attractive, particularly for parameters describing heterogeneity. A recent non-Bayesian approach to fixed-effects meta-analysis provides novel ways to think about…

There exists a variety of interval estimators for the overall treatment effect in a random-effects meta-analysis. A recent literature review summarizing existing methods suggested that in most situations, the Hartung-Knapp/Sidik-Jonkman (HKSJ) method was preferable. However, a quantitative comparison of those methods in a common simulation study…

The ability of a scientific discipline to build cumulative knowledge depends on its predominant method of data analysis. A steady accumulation of knowledge requires approaches which allow researchers to consider results from comparable prior research. Bayesian statistics is especially relevant for establishing a cumulative scientific discipline,…

Researchers may have multiple motivations for combining disparate pieces of evidence in a meta-analysis, such as generalizing experimental results or increasing the power to detect an effect that a single study is not able to detect. However, while in meta-analysis, the main question may be simple, the structure of evidence available to answer it…