Meta-Regression

Abstract

In classical meta-analysis, a two-stage procedure was generally employed. In the first stage, we estimate the “mean treatment effect” of each study by the summarized data or extract it from the original study directly; Then in the second stage, we combine the “mean treatment effect” of each study and obtain the “weighted mean treatment effect”. The ideal assumption is that for all the studies the samples are from the same population with the same age, gender proportion, region, body mass index etc while, in practice, these study populations are unlikely to be the same. Suppose we treat the “mean treatment effect” of each study as a dependent variable, and the mean value of these study population characteristics as independent variables, and then establish a study-level regression analysis. If there is an association between them, these independent variables can be regarded as study-level moderators, which have a potential impact on the pooled effect. That is, the “mean treatment effect” changes as the status of these characteristics change, and these characteristics can then be considered a source of heterogeneity between studies, with the average “treatment effect” in the regression being the moderator adjusted effect (Higgins et al. 2003). We call regression analysis based on study-level data a meta-regression (Borenstein et al. 2011).KeywordsMeta-regressionAggregate dataHeterogeneity