Dear all,
I'm struggling how to treat different indicators of the association that I want to examine in my meta-analysis, which are
- correlations (this is clear to me)
- partial correlations (I assume controlling for covariates can be considered as a moderator)
- standardised regression coefficients from various regression models
- betas from propensity score matching
- standardised coefficients from SEMs
Can all standardised Betas be treated the same way referring to calculating effect sizes?
Thank you very much in advance for your help,
Katrin
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Von: R-sig-meta-analysis <r-sig-meta-analysis-bounces at r-project.org> Im Auftrag von r-sig-meta-analysis-request at r-project.org
Gesendet: Donnerstag, 2. August 2018 12:04
An: r-sig-meta-analysis at r-project.org
Betreff: R-sig-meta-analysis Digest, Vol 15, Issue 2
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Today's Topics:
1. Re: Fwd: SV: Re:: Question: Metafor R package - rma and
rma.mv function (Simona Frederiksen)
----------------------------------------------------------------------
Message: 1
Date: Wed, 1 Aug 2018 11:16:29 +0000
From: Simona Frederiksen <simona.frederiksen at hotmail.com>
To: "Viechtbauer, Wolfgang (SP)"
<wolfgang.viechtbauer at maastrichtuniversity.nl>,
"r-sig-meta-analysis at r-project.org"
<r-sig-meta-analysis at r-project.org>
Subject: Re: [R-meta] Fwd: SV: Re:: Question: Metafor R package - rma
and rma.mv function
Message-ID:
<DB7PR08MB332428001B4ED9EF1F9D95ED8E2D0 at DB7PR08MB3324.eurprd08.prod.outlook.com>
Content-Type: text/plain; charset="utf-8"
Dear Wolfgang,
Thank you very much for your reply and the link. I just came back from Scotland.
I read through the link and I am wondering if I should skip est_id if I find it non-significant?
Regarding the weights, I am not quite aware of how this is computed since 1/vi does not result in the weights that I can extract from weights(res). The model that I am fitting is it as follows: yi = mean + uj[i] + ei for the i th comparison and the j th study when using the rma.mv function for the full model (=inclusion of est_id in addition to study id)? And how would it look if not including est_id? Finally, I am wondering how the weights are applied to the model and how it is calculated?
Best wishes,
Simona
________________________________
Fra: Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl>
Sendt: Monday, July 30, 2018 12:47:30 PM
Til: Simona Frederiksen; r-sig-meta-analysis at r-project.org
Emne: RE: [R-meta] Fwd: SV: Re:: Question: Metafor R package - rma and rma.mv function
Dear Simona,
Did you see my reply?
https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2018-July/000949.html
Best,
Wolfgang
-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On Behalf Of Simona Frederiksen
Sent: Wednesday, 25 July, 2018 22:45
To: r-sig-meta-analysis at r-project.org
Subject: [R-meta] Fwd: SV: Re:: Question: Metafor R package - rma and rma.mv function
Dear Michael,
Thank you for your fast reply. It is very appreciated. I should have changed it to plain text now.
Maybe I am not quite aware of how this is computed. The model that I am fitting is it as follows: yi = mean + uj[i] + ei for the i th comparison and the j th study when using the rma.mv function? And how is the weights applied to it or calculated?
I tried to use the profile.rma.mv() function and I got one maxima. Can I then assume that it is converged?
Best wishes,
Simona
________________________________
Fra: Michael Dewey <lists at dewey.myzen.co.uk<mailto:lists at dewey.myzen.co.uk>>
Sendt: Wednesday, July 25, 2018 3:06:06 PM
Til: Simona Frederiksen; r-sig-meta-analysis at r-project.org<mailto:r-sig-meta-analysis at r-project.org>
Emne: Re:: [R-meta] Question: Metafor R package - rma and rma.mv function
Simona Frederiksen <simona.frederiksen at hotmail.com<mailto:simona.frederiksen at hotmail.com>> wrote :
Dear Simona
Given the calculated variances (vi) the weights seem quite OK to me.
Would the question underlying the convergence issue be better answered by using profile.rma.mv()?
Please set your mailer to post in plain text not HTML as your post got mangled which made running your example rather tricky.
Michael.
Hi,
I have some question sthat I would like to get posted regarding the
metafor R package and the rma and rma.mv function. It goes like this:
Hi,
I am at the moment working in the R package 'metafor' in order to
perform a meta-analysis which I carry out in collaboration with the
Danish Headache Center. I just figured that I have to use the rma.mv
function since I have several effect sizes per study (for two studies).
I have following questions that I hope you can help me answer:
1. When I calculate the weights for each study, the studies that
have more effect sizes receive a really high weight compared to the
other studies with just one effect size. So it seems that these
studies primarily are used to calculate the overall effect size even though N seems to be quite small?
1. How can I see convergence when I use the rma.mv function? When
using the rma function, it turns up when adding verbose = T. And if it
does not converge, what would be ideal to do?
Here is an example:
library("metafor")
study_id
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