[R-meta] rma.mv only for better SEs

[Simon Harmel]

Thank you, Wolfgang. I asked this, because I noticed applying RVE to an
rma.mv() model has no bearing on the estimates of fixed effects themselves,
and just modifies their SEs.

So, I wondered if the same rule, at least "in principle", should apply when
we go from rma() to rma.mv().

But is there a principle regarding how random effects affect the fixed
effects?

For instance, in:

1- rma.mv(y ~ 1, random = ~ 1|study/obs), the overall average only
represents the average of study-level effects.

But, in:

2- rma.mv(y ~ 1, random = ~ 1|study/outcome/obs), the overall average
represents the average of study-level effects additionally affected by the
outcome-level effects within them.

And thus, 1- and 2- may give different overall averages, right?

Simon


On Mon, Jan 31, 2022 at 11:00 AM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:

> Generally, two models with different random effects structures will also
> give you different estimates of the fixed effects (unless the estimates of
> the variance/covariance components happen to be such that the two models
> collapse down to the same structure).
>
> Best,
> Wolfgang
>
> >-----Original Message-----
> >From: R-sig-meta-analysis [mailto:
> r-sig-meta-analysis-bounces using r-project.org] On
> >Behalf Of Simon Harmel
> >Sent: Monday, 31 January, 2022 17:49
> >To: R meta
> >Subject: [R-meta] rma.mv only for better SEs
> >
> >Hello List Members,
> >
> >Reviewing the archived posts, my understanding is that my studies can
> >produce multiple effects, so I should use rma.mv() not rma().
> >
> >Also, I understand rma.mv() ensures that I get more accurate SEs for my
> >fixed effects relative to rma().
> >
> >BUT does that also mean that, by definition, rma.mv() should have no
> >bearing on the magnitude of the fixed effects themselves and only modifies
> >their SEs relative to rma()?
> >
> >Thank you,
> >Simon
>

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