I'm conducting a meta-analysis where I have a given set of studies, each with multiple effects (correlations in my case) I'm interested in. Each correlation coefficient answers a different research question, so I'm actually conducting many meta-analyses, one per effect... For each effect I've performed the following (I FDR-correct everything at the end): I've first used a random effects model (metafor package) to estimate the overall effect. Then, using the rma function, I re-ran the model this time using moderators. So I end up with estimations of the overall effect and its significance, as well as heterogenity estimations, from the basic model. Then I also obtain indication of moderators that are significantly associated with the effect. My question is about interpreting these two together.
What's not clear to me, from the meta-regression results, is what can I learn about the overall effect? Is it possible that one moderator will come out significant, but looking at the forest plot - none of the correlations were actually significant? (i.e. all had huge confidence intervals crossing the zero-effect line)?
Secondly, is this a legit approach? First identifying significant effects, then examining significant moderators even if the effect by itself did not come out significant?