-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org]
On Behalf Of Tarun Khanna
Sent: Friday, 12 June, 2020 16:15
To: r-sig-meta-analysis at r-project.org
Subject: Re: [R-meta] R-sig-meta-analysis Digest, Vol 37, Issue 23
Thanks for the explnation Wolfgang. I also found it useful.
In your reply you mention that - "We can ignore those correlations and use
the multilevel model as a working model that is an approximation to the
model that also accounts for correlated sampling errors. After fitting the
multilevel model with rma.mv(), one can then use cluster robust inference
methods to 'fix things up'."
Do you have an example study that does this?
Best
Tarun
Tarun Khanna
PhD Researcher
Hertie School
Friedrichstra?e 180
10117 Berlin ? Germany
khanna at hertie-school.org ? www.hertie-school.org<http://www.hertie-
school.org/>
Date: Thu, 11 Jun 2020 13:33:02 +0000
From: "Viechtbauer, Wolfgang (SP)"
<wolfgang.viechtbauer at maastrichtuniversity.nl>
To: Norman DAURELLE <norman.daurelle at agroparistech.fr>
Cc: r-sig-meta-analysis <r-sig-meta-analysis at r-project.org>
Subject: Re: [R-meta] weight in rmv metafor
Message-ID: <1b8c1463bcdf43baaa39788aa2a859c1 at UM-MAIL3214.unimaas.nl>
Content-Type: text/plain; charset="iso-8859-1"
Dear Norman,
To give a simple example: When (some of the) studies contribute multiple
estimates, the dataset has a multilevel structure (with estimates nested
within studies). A common way to deal with this is to fit a multilevel model
with random effects for studies and estimates within studies. Like this:
http://www.metafor-project.org/doku.php/analyses:konstantopoulos2011
However, multiple estimates from the same study are actually often computed
based on the same sample of subjects. In that case, the sampling errors are
also correlated. The multilevel model does not capture this. For this, one
would ideally want to fit a model that also allows for correlated sampling
errors. Like this:
http://www.metafor-project.org/doku.php/analyses:berkey1998
However, computing the covariances between the sampling errors within a
study is difficult and requires information that is often not available.
We can ignore those correlations and use the multilevel model as a working
model that is an approximation to the model that also accounts for
correlated sampling errors. After fitting the multilevel model with
rma.mv(), one can then use cluster robust inference methods to 'fix things
up'.
Quite a bit of this has been discussed at length in previous posts on this
mailing list. You might want to search the archives for some of these posts.
Best,
Wolfgang
-----Original Message-----
From: Norman DAURELLE [mailto:norman.daurelle at agroparistech.fr]
Sent: Thursday, 11 June, 2020 15:05
To: Viechtbauer, Wolfgang (SP)
Cc: r-sig-meta-analysis
Subject: Re: [R-meta] weight in rmv metafor
Thank you.
I am not sure I understand exactly what you mean by " if the working model
is only an approximation and doesn't cover all dependencies ".
Could you please explain it ?
For now I used the rma() function to synthesize the available literature
existing on the blackleg - oil seed rape disease-yield relationship, using
slopes as effect-sizes.
the models that gave me the slopes I used in the meta-analysis are all Y =
+ bX, simple linear regressions with Y being the yield and X being the
diseqse severity.
So my slopes, b, are all negative, and I have obtained a "summary" effect
size through the rma() function.
But I indeed have two studies that for now contribute to most of the
sizes that are included in my meta-analysis.
So why exactly is it necessary to use the rma.mv() function ?
What exactly does the "multivariate" qualificative refer to ?
Thank you,
Norman.