[R-meta] meta-analysis on different association-indicators with metafor
k@trin@wolf2 @ending from fu-berlin@de
Fri Aug 3 15:31:24 CEST 2018
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,
Von: R-sig-meta-analysis <r-sig-meta-analysis-bounces using r-project.org> Im Auftrag von r-sig-meta-analysis-request using r-project.org
Gesendet: Donnerstag, 2. August 2018 12:04
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1. Re: Fwd: SV: Re:: Question: Metafor R package - rma and
rma.mv function (Simona Frederiksen)
Date: Wed, 1 Aug 2018 11:16:29 +0000
From: Simona Frederiksen <simona.frederiksen using hotmail.com>
To: "Viechtbauer, Wolfgang (SP)"
<wolfgang.viechtbauer using maastrichtuniversity.nl>,
"r-sig-meta-analysis using r-project.org"
<r-sig-meta-analysis using r-project.org>
Subject: Re: [R-meta] Fwd: SV: Re:: Question: Metafor R package - rma
and rma.mv function
<DB7PR08MB332428001B4ED9EF1F9D95ED8E2D0 using DB7PR08MB3324.eurprd08.prod.outlook.com>
Content-Type: text/plain; charset="utf-8"
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?
Fra: Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer using maastrichtuniversity.nl>
Sendt: Monday, July 30, 2018 12:47:30 PM
Til: Simona Frederiksen; r-sig-meta-analysis using r-project.org
Emne: RE: [R-meta] Fwd: SV: Re:: Question: Metafor R package - rma and rma.mv function
Did you see my reply?
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On Behalf Of Simona Frederiksen
Sent: Wednesday, 25 July, 2018 22:45
To: r-sig-meta-analysis using r-project.org
Subject: [R-meta] Fwd: SV: Re:: Question: Metafor R package - rma and rma.mv function
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?
Fra: Michael Dewey <lists using dewey.myzen.co.uk<mailto:lists using dewey.myzen.co.uk>>
Sendt: Wednesday, July 25, 2018 3:06:06 PM
Til: Simona Frederiksen; r-sig-meta-analysis using r-project.org<mailto:r-sig-meta-analysis using r-project.org>
Emne: Re:: [R-meta] Question: Metafor R package - rma and rma.mv function
Simona Frederiksen <simona.frederiksen using hotmail.com<mailto:simona.frederiksen using hotmail.com>> wrote :
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.
> 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:
> 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:
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