[R-meta] rma.mv only for better SEs
This is very helpful, thank you so very much. Simon ps. This may be loosely relevant but in ordinary multilevel models, we don't use weights, but still random-effects' structures do have a bearing on the fixed effect estimates. So, aside from weights, something else from random-effects must have an impact on fixed-effect magnitude. On Mon, Jan 31, 2022 at 2:04 PM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
Right, sorry, that was a typo. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 19:29 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs Sure, but didn't you by any chance mean to say: "The random effects structure determines the weight matrix, which in turn
affects
the estimates of the **fixed effects**". On Mon, Jan 31, 2022 at 12:23 PM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: The random effects structure determines the weight matrix, which in turn
affects
the estimates of the random effects. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Monday, 31 January, 2022 18:29 To: Viechtbauer, Wolfgang (SP) Cc: R meta Subject: Re: [R-meta] rma.mv only for better SEs I have done it, and in my case the results differ. But my point was, is
my
explanation regarding why they differ accurate? On Mon, Jan 31, 2022 at 11:24 AM Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: 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