Message-ID: <3d1e87523cf748ceae9068e0624461ee@UM-MAIL3214.unimaas.nl>
Date: 2022-01-31T17:24:50Z
From: Wolfgang Viechtbauer
Subject: [R-meta] rma.mv only for better SEs
In-Reply-To: <CACgv6yWTUJ-XVukTMegf9-+UmiMU3JVPYhXTVB-G_iYXAiHmTw@mail.gmail.com>
Just try it out and you will see what happens.
Best,
Wolfgang
>-----Original Message-----
>From: Simon Harmel [mailto:sim.harmel at gmail.com]
>Sent: Monday, 31 January, 2022 18:21
>To: Viechtbauer, Wolfgang (SP)
>Cc: R meta
>Subject: Re: [R-meta] rma.mv only for better SEs
>
>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 at 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 at 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