[R-meta] Performing a multilevel meta-analysis

Thu, Aug 20, 2020 5:16 PM

Dear Wolfgang,

Yes, indeed.
It's a computation of a number formed from the original within or between
individuals SD. Therefore, I assume (as Fernando suggested) that the most
reasonable method to choose is the log of the SD with bias correction, then
using multilevel analysis with an extension of robust variance estimation
(considering the structure of my dataset).
Log-transformation was used in the recent studies analysed similar values.

P.S: I know you've already said that you don't have clear suggestions in
regards to this.

Thanks for the assistance!

Kind regards,

Tzlil Shushan | Sport Scientist, Physical Preparation Coach

BEd Physical Education and Exercise Science
MSc Exercise Science - High Performance Sports: Strength &
Conditioning, CSCS
PhD Candidate Human Performance Science & Sports Analytics



??????? ??? ??, 20 ????? 2020 ?-21:06 ??? ?Viechtbauer, Wolfgang (SP)?? <?
wolfgang.viechtbauer at maastrichtuniversity.nl??>:?

Leaving aside that the SEM, as far as I understood your description of it,
is not just a 'simple' standard deviation (i.e., it is computed in a
different way) - yes, that is how you should specify the arguments for this
outcome measure.

Best,
Wolfgang

-----Original Message-----
From: Tzlil Shushan [mailto:tzlil21092 at gmail.com]
Sent: Wednesday, 19 August, 2020 16:21
To: Fernando Klitzke Borszcz
Cc: Viechtbauer, Wolfgang (SP); r-sig-meta-analysis at r-project.org
Subject: Re: [R-meta] Performing a multilevel meta-analysis

Dear Wolfgang and Fernando,

Apologise for the multiple emails, but I just figured out that my last
questions were probably unnecessary..
After I read this ?measures for quantitative variables? section?
https://wviechtb.github.io/metafor/reference/escalc.html
I finally understood that I probably need to specify the SEM values as sdi
and sample size as ni in the model.
res -> escalc(measure = ?SDLN?, sdi = sem, ni, data = dat)
That?s right?

Thanks and kind regards,

On Wed, 19 Aug 2020 at 21:28, Tzlil Shushan <tzlil21092 at gmail.com> wrote:
Dear Wolfgang and Fernando,

Woflgang, thanks for letting me know..

Fernando, thanks for your answer, I wanted to have some time working with
"SDLN" function you suggested before commenting again.

I'm familiar with those papers that investigated SEM, thanks for sending
them over. Since you already mentioned the "SDLN" function I have two
questions;

1) If I want to proceed with log transformation of SEM effect sizes, Do I
need to specify log() for the yi value? res <- escalc(measure = "SDLN",

yi =

log(sem), vi , data = dat)?

2) Because it is hard to obtain the sampling variance for each individual
study (some reported CI and some not), What function should I use to

compute

the sampling variance? is 1/(n-3) works fine in this case?

If I be able to compute the estimated standard error from individual

studies

based on their confidence intervals: (CI upper - CI lower)/3.92 for 95%

CI,

then specify sei within the escalc function to compute the variance. Does
this approach serve better estimation for the model?

Kind regards,

Tzlil Shushan | Sport Scientist, Physical Preparation Coach

BEd Physical Education and Exercise Science
MSc Exercise Science - High Performance Sports: Strength &
Conditioning, CSCS
PhD Candidate Human Performance Science & Sports Analytics

[R-meta] Performing a multilevel meta-analysis

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