[R-meta] random part in meta-regression vs. that in multilevel models

[Jack Solomon]

Hello List Members,

**First, I have always thought it is illegitimate to add random-effects for
something that has not been estimated in the fixed part of the model.  For
example:

`lme4::lmer(math ~ female*minority + (ses | sch.id), data = data)` is
illegitimate because `ses` has not been estimated in the fixed part.

But I frequently see multilevel meta-regression models where intercept is
dropped (~0+...) from the fixed part but at the same time it is added to
the random part.  For example:

metafor::rma.mv(es ~ 0+outcome, V, random= ~1|id/outcome, data = data)

>>>>>> So, why is this ok in meta-regression?

**Second, I have always thought that `outcome` is treated as a categorical
predictor and thus appearing only to the **left** of `|`.  For example:

lme4::lmer(es ~0+outcome + (0 + outcome | id), data = data)

But I frequently see multilevel meta-regression models where outcome is
treated as a categorical predictor AND a **grouping variable** thus
appearing only to the **right** of `|`.  For example:

metafor::rma.mv(es ~ 0+outcome, V, random= ~1|id/outcome, data = data)

>>>>>> So, why is this ok in meta-regression?

Many thanks for your support,
Jack

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