## Model Discrimination in Meta-Analysis - A Bayesian Perspective

### BibTeX

@MISC{Abrams_modeldiscrimination,

author = {Keith Abrams and Bruno Sansó},

title = {Model Discrimination in Meta-Analysis - A Bayesian Perspective},

year = {}

}

### OpenURL

### Abstract

In wanting to summarise evidence from a number of studies a variety of statistical methods have been proposed. Of these the most widely used is the so-called fixed effect model in which the individual studies are estimating a single, but unknown, overall population effect. When there is `considerable' heterogeneity, in terms of the effect sizes, between the studies the use of a random effect model has been advocated in which each individual study is assumed to be estimating its own, unknown, true effect. Discrimination between fixed and random effect models has been advocated by means of a Ø 2 test for heterogeneity, which it is accepted has low statistical power. Recent interest has been shown in the use of Bayes Factors as an alternative. The use of Bayes factors is illustrated using a number of previously published meta-analyses in which there are varying degrees of heterogeneity. It is shown how the use of Bayes Factors leads to a more intuitive assessment of the evidence in favo...