Meta-analysis entails the combination of different studies or datasets on the same research question and meta-analytic methods have been used across many different scientific disciplines [1], [2]. Early applications of meta-analysis in the 1970s and 1980s proposed that a major gain from these methods was the ability to improve power and obtain more definitive summary results by combining several small studies [3]. However, it soon became evident that simply focusing on summary effects could be misleading. For epidemiological applications in particular, a major threat is that the precision derived from combining data may be spurious, especially if the combined studies and datasets have considerable dissimilarities [4]. It is well appreciated now that besides estimating summary effects, estimation and, if possible, explanation, of the between-study heterogeneity is a very important goal for meta-analysis [5].
