We identify a number of procedural, conceptual and theoretical issues that need to be addressed in moving forward with this area, and emphasise the need for existing techniques to be evaluated and modified, rather than inventing new approaches.

This paper reviews the use of Bayesian methods in meta-analysis. Whilst there has been an explosion in the use of meta-analysis over the last few years, driven mainly by the move towards evidence-based healthcare, so too Bayesian methods are being used increasingly within medical statistics. Whilst in many meta-analysis settings the Bayesian models used mirror those previously adopted in a frequentist formulation, there are a number of specific advantages conferred by the Bayesian approach. These include: full allowance for all parameter uncertainty in the model, the ability to include other pertinent information that would otherwise be excluded, and the ability to extend the models to accommodate more complex, but frequently occurring, scenarios. The Bayesian methods discussed are illustrated by means of a meta-analysis examining the evidence relating to electronic fetal heart rate monitoring and perinatal mortality in which evidence is available from a variety of sources.

In meta-analysis, when the difference in results between studies is greater than would be expected by chance, one needs to investigate whether the observed variation in results across studies is associated with clinical and/or methodological differences between studies. This article reviews methods used in meta-analysis for exploring heterogeneity, including statistical tests for homogeneity, methods for visually displaying results of primary studies, methods for reducing heterogeneity, methods for investigating sources of heterogeneity, and identification of moderator variables or effect modifiers. The investigation of sources of heterogeneity in meta-analysis is by nature exploratory, and therefore its results should always be interpreted with caution. However, careful investigation of heterogeneity may provide an important second level of evidence that can be useful in suggesting direction of future research. Sometimes, it may provide clinically important results by indicating who might benefit more or less from a treatment or how an intervention should be applied.

No abstract