10.6 Meta-regression

The examples in this chapter have been exclusively concerned with using meta-analysis to produce an integrated estimate of an effect size from several datasets. This is only a small part of the use of meta-analysis though. More often, you want to see if methodological or other features of the studies explain variation in their effect sizes. This is called meta-regression. In meta-regression, you are treating the effect size as an outcome variable, and trying to predict it from an intercept, plus a number of predictor variables each of which represents something about the study.

You need more than five effect sizes to do meta-regression meaningfully, which is why I have not gone into it here. But the general outline is simple: you add extra columns to your data frame of effect sizes, in which you record your candidate predictors of variation in effect size. You then add these variables to your call to the rma() function using the argument mods = ~ variable1 + variable 2....