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[R-meta] Overlapping CIs with significant difference among subgroups

Dear Rafael,

The SEs of the predicted average outcomes for the various levels can be quite different than the SEs of the difference between levels.

You can get the predicted average outcome for all four levels with:

pred.r = predict(res, transf=transf.ztor, digits=3, newmods = rbind(c(0,0,0), c(1,0,0), c(0,1,0), c(1,1,1)))
pred.r

And then you can plot them with:

forest(pred.r$pred, ci.lb=pred.r$ci.lb, ci.ub=pred.r$ci.ub, slab=c("Without grouping","Temporal grouping","Spatial grouping", "Both"))

Indeed, CIs are wide and overlap. But let's now compute the predicted average difference between levels when compared against the "Without grouping" level:

pred.r = predict(res, transf=transf.ztor, digits=3, newmods = rbind(c(1,0,0) - c(0,0,0), c(0,1,0) - c(0,0,0), c(1,1,1) - c(0,0,0)), intercept=FALSE)
pred.r

forest(pred.r$pred, ci.lb=pred.r$ci.lb, ci.ub=pred.r$ci.ub, slab=c("Diff Temporal grouping","Diff Spatial grouping", "Diff Both"))

Now the CIs are quite narrow and exlude 0.

This aside, I would recommend that you include random effects for species twice, once without and once with the phylogenetic correlation matrix:

h$speciesIDnon <- h$speciesID
res <- rma.mv(zf, vzf, mods=~sce_temporal*sce_spatial, random = list (~1|effectsizeID, ~1|studyID, ~1|speciesIDnon, ~1|speciesID), R=list(speciesID=corr), data=h)

This is model (15) from:

Nakagawa, S., & Santos, E. S. A. (2012). Methodological issues and advances in biological meta-analysis. Evolutionary Ecology, 26(5), 1253-1274.

Conclusions do not change as far as I can tell, but I would still go with that model. A LRT also shows that this model fits significantly better:

res0 <- rma.mv(zf, vzf, mods=~sce_temporal*sce_spatial, random = list (~1|effectsizeID, ~1|studyID, ~1|speciesID), R=list(speciesID=corr), data=h)
anova(res, res0)

Best,
Wolfgang