-----Original Message-----
From: Tzlil Shushan [mailto:tzlil21092 at gmail.com]
Sent: Saturday, 15 August, 2020 5:10
To: Viechtbauer, Wolfgang (SP)
Cc: r-sig-meta-analysis at r-project.org
Subject: Re: [R-meta] Performing a multilevel meta-analysis
Dear Wolfgang,
First, thank you so much for the quick response and the time you dedicate to
my questions. And yes, I looked on the mailing list and have seen some
meaningful discussions around some of my questions. Based on the readings, I
assume that an extension of my multilevel model with robust variance
inference is a good idea.
However, I still would like to give a chance to the second question I had
and I'll try to be more specific this time.?I hope you (or others in this
group) can help me with that.
One of the effect sizes in the meta-analysis is the 'standard error of
measurement' (SEM) of heart rate from a test-retest (reliability)
assessment. Simply described, this assessment was performed twice on a
matched group and I'm interested in the variability of this measure. This
effect size is derived from the pooled standard deviation (mean test-retest
SD) and intraclass correlation (ICC) of a test-retest. For example, if the
mean ? SD of test one is 80.0 ? 4.0 and test two is 80.5 ? 4.8, and
intraclass correlation is 0.95, the SEM will be 4.4*?(1-0.95)= 0.98.
Practically, this effect size is a form of SD value.
I'm aware of the fact that the first thing that I probably should do if I
want to use metafor package is to convert these values into coefficient of
variation (CV%). However, because the outcome measure (heart rate) is
already calculated?in percentages values (% of heart rate maximum), we'd
like to meta-analyse the SEM in the original raw values. Further, using this
effect size is important for having practical implications in the paper.
I've seen some discussion in the mailing
list?https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2018-
May/000828.html?fbclid=IwAR2dSpruCCqlk631VKBAflkibrD8Gke-
9sSGgMHxG4TtY_ocZX1IsZCPlI0?on CV% from matched groups
with?escalc(measure="CVRC",?y = logCV_1 - logCV_2). However, I'd like to
know if there is a way to fit the escalc equation to the SEM values (which
is only one value from each paired test)? or alternatively, if there are
other approaches I should consider?
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
??????? ??? ??, 12 ????? 2020 ?-4:46 ??? ?Viechtbauer, Wolfgang (SP)??
<?wolfgang.viechtbauer at maastrichtuniversity.nl??>:?
Dear Tzlil,
Your questions are a bit too general for me to give meaningful answers.
Also, some of your questions (with regard to modeling dependent effects and
using cluster robust methods) have been extensively discussed on this
mailing list, so no need to repeat all of that. But yes, if you use cluster
robust inference methods, I would use them not just for the 'overall model'
but also for models including moderators.
Best,
Wolfgang
-----Original Message-----
From: Tzlil Shushan [mailto:tzlil21092 at gmail.com]
Sent: Thursday, 06 August, 2020 16:05
To: Viechtbauer, Wolfgang (SP)
Cc: r-sig-meta-analysis at r-project.org
Subject: Re: [R-meta] Performing a multilevel meta-analysis
Dear?Wolfgang,
Thanks for your quick reply and sorry in advance for the long ?assay?..
It is probably be better if I give an overview on my analysis. Generally, I
employ meta-analysis on the reliability and validity of heart rate response
during sub-maximal assessments. We were able to compute three different
effect sizes reflects reliability; mean differences, ICC and standard error
of measurement of test-retest design, while for validity, we computer
correlation coefficient between heart rate values and maximal aerobic
fitness.
Since both measurement properties (i.e reliability/validity) of heart rate
can be analysed from different intensities during the assessment (for
example, 70, 80 and 90% from heart rate maximum), different modalities of
tests (e.g running, cycling), and multiple time points across the year
before season, in-season), one sample can have more than one effect size.
I decided to employ three level meta-analysis, while level two and three
pertaining to within and between samples variance, respectively. Then,
include moderators effect within and between samples).
Regarding the weights, the only reason I wonder if I need to adjust them is
because the wide range of effect sizes per sample (1-4 per sample) and
thought to use the approach you discussed in your recent post here.
http://www.metafor-project.org/doku.php/tips:weights_in_rma.mv_models
However, as I understand the default W in rma.mv will work quite well?
With regards to the above (i.e multiple effect sizes for samples), I
consider to add robust cluster test to get more accurate standard error
values. As I understand, it may be a good option to control for the natural
(unknown) correlations between effect sizes from the same sample.
First, do you think it is necessary? If so, would you apply cluster test
just to the overall model or for additional models including moderators.
Second, Is it reasonable to report the results obtained from the multilevel
and cluster analyses in the paper?
Of note, my dataset isn?t large and includes between 15-20 samples
(clusters) while around 50-60% have multiple effect sizes.
With regards to the second question in the original email, we computer the
standard error of measurement (usually attained from pooled SD of test-
retest multiply the square root of 1-icc). Practically, these effect sizes
are sd values. I haven?t seen enough meta-analysis studies using standard
error of measurement as effect size and I speculate if you can suggest me
what would be a decent approach for this?
Cheers,
On Thu, 6 Aug 2020 at 22:30, Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
Dear Tzlil,
Unless you have good reasons to do so, do not use custom weights. rma.mv()
uses weights and the default ones are usually fine.
weights(res, type="rowsum") will only (currently) work in the 'devel'
version of metafor, which you can install as described here:
https://wviechtb.github.io/metafor/#installation
I can't really comment on the second question, because answering this would
require knowing all details of what is being computed/reported.
As for the last question ("is there a straightforward way in metafor to
specify the analysis with Chi-square values"): No, chi-square values are
test statistics, not an effect size / outcome measure, so they cannot be
used for a meta-analysis (at least not with metafor).
Best,
Wolfgang
-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-
On Behalf Of Tzlil Shushan
Sent: Wednesday, 05 August, 2020 5:45
To: r-sig-meta-analysis at r-project.org
Subject: [R-meta] Performing a multilevel meta-analysis
Hi R legends!
My name is Tzlil and I'm a PhD candidate in Sport Science - Human
performance science and sports analytics
I'm currently working on a? multilevel meta-analysis using the metafor
package.
My first question is around the methods used to assign weights within
models.
I'd like to know if there is a conventional or 'most conservative'
to continue with. Since I haven't found a consistent methodology within
multilevel meta-analyses papers I read, I originally applied a weight
pertains to variance (vi) and number of effect sizes from the same study.
my model..
For some reason, I tried to imitate the approach used in the first link
above. However, for some reason I get an error every time I tried to
specify weights(res, type="rowsum") *Error in match.arg(type,
"matrix")) : 'arg' should be one of ?diagonal?, ?matrix?*
My second question is related to the way I meta-analyse a specific ES. My
meta-analysis involves the reliability and convergent validity of heart
rate during a specific task, which is measured in relative values (i.e.
percentages). Therefore, my meta-analysis includes four different ESs
parameters (mean difference; MD, interclass correlation; ICC, standard
error of measurement; SEM, and correlation coefficient; r).
I wonder how I need to use SEM before starting the analysis. I've seen
papers which squared and log transformed the SEM before performing a
meta-analysis, while others converted the SEM into CV%. Due to the
scale of our ES (which is already in percentages) I'd like to perform the
analysis without converting it into CV% values. Should I use the SEM as
reported values? only log transformed it? Further, is there a
straightforward way? in metafor to specify the analysis with Chi-square
values (as "ZCOR" in correlations)?
Thanks in advance!
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