[R-meta] R-sig-meta-analysis Digest, Vol 37, Issue 23
Viechtbauer, Wolfgang (SP)
wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Fri Jun 12 16:24:23 CEST 2020
Here is an example:
dat <- dat.berkey1998
res <- rma.mv(yi, vi, mods = ~ outcome - 1, random = ~ outcome | trial, struct="UN", data=dat)
>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org]
>On Behalf Of Tarun Khanna
>Sent: Friday, 12 June, 2020 16:15
>To: r-sig-meta-analysis using 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?
>10117 Berlin ∙ Germany
>khanna using hertie-school.org ∙ www.hertie-school.org<http://www.hertie-
>Date: Thu, 11 Jun 2020 13:33:02 +0000
>From: "Viechtbauer, Wolfgang (SP)"
> <wolfgang.viechtbauer using maastrichtuniversity.nl>
>To: Norman DAURELLE <norman.daurelle using agroparistech.fr>
>Cc: r-sig-meta-analysis <r-sig-meta-analysis using r-project.org>
>Subject: Re: [R-meta] weight in rmv metafor
>Message-ID: <1b8c1463bcdf43baaa39788aa2a859c1 using UM-MAIL3214.unimaas.nl>
>Content-Type: text/plain; charset="iso-8859-1"
>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:
>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:
>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
>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.
>>From: Norman DAURELLE [mailto:norman.daurelle using agroparistech.fr]
>>Sent: Thursday, 11 June, 2020 15:05
>>To: Viechtbauer, Wolfgang (SP)
>>Subject: Re: [R-meta] weight in rmv metafor
>>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
>>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 ?
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