[R-meta] Guidance regarding balance in fixed- and random-effects

[Viechtbauer, Wolfgang (SP)]

If the estimate of sigma^2 for lab is zero, then 

rma.mv(yi, vi, random = ~ 1 | lab / study / outcome / time / rowID)

and 

rma.mv(yi ~ mod1*mod2, vi, random = ~ 1 | study / time / rowID)

are identical. So I would not bother to manually drop the lab random effect, since in essence this happens automatically.

In general, I would use (1) for all analyses since this is the a priori chosen model that is meant to reflect the dependencies and sources of heterogeneity you think may be relevant. If some components end up being 0 in some models, then so be it, but I would stick to one model for all analyses.

Best,
Wolfgang

>-----Original Message-----
>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On
>Behalf Of Luke Martinez
>Sent: Wednesday, 13 October, 2021 23:26
>To: R meta
>Subject: [R-meta] Guidance regarding balance in fixed- and random-effects
>
>Dear Experts,
>
>Forgive my modeling question. But in answering RQs like: what is the
>overall effect of X, I often fit an intercept-only model with several
>nested levels, like:
>
>(1) rma.mv(yi, vi, random = ~ 1 | lab / study / outcome / time / rowID)
>
>In the above model, all levels reveal heterogeneity in them.
>
>But then in answering other RQs, when I add a couple of moderators,
>some of the levels (e.g., "outcome" AND "lab") return ZERO
>heterogeneity making me fit a simpler model, like:
>
>(2) rma.mv(yi ~ mod1*mod2, vi, random = ~ 1 | study / time / rowID)
>
>Question: When this happens, does this mean that I should go back and
>refit model (1) without "outcome" AND "lab" to uniform the random
>specification of model (1) and model (2)?
>
>OR, model (1) is appropriate for RQ1 and model (2) is appropriate for RQ2s?
>
>Thank you for your perspectives,
>Luke