[R-meta] Performing a multilevel meta-analysis
tz|||21092 @end|ng |rom gm@||@com
Sat Aug 15 05:10:19 CEST 2020
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
I've seen some discussion in the mailing list
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?
Tzlil Shushan | Sport Scientist, Physical Preparation Coach
BEd Physical Education and Exercise Science
MSc Exercise Science - High Performance Sports: Strength &
PhD Candidate Human Performance Science & Sports Analytics
בתאריך יום ד׳, 12 באוג׳ 2020 ב-4:46 מאת Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using 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.
> >-----Original Message-----
> >From: Tzlil Shushan [mailto:tzlil21092 using gmail.com]
> >Sent: Thursday, 06 August, 2020 16:05
> >To: Viechtbauer, Wolfgang (SP)
> >Cc: r-sig-meta-analysis using 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,
> >employ meta-analysis on the reliability and validity of heart rate
> >during sub-maximal assessments. We were able to compute three different
> >effect sizes reflects reliability; mean differences, ICC and standard
> >of measurement of test-retest design, while for validity, we computer
> >correlation coefficient between heart rate values and maximal aerobic
> >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
> >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.
> >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
> >(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
> >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?
> >On Thu, 6 Aug 2020 at 22:30, Viechtbauer, Wolfgang (SP)
> ><wolfgang.viechtbauer using 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:
> >I can't really comment on the second question, because answering this
> >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).
> >>-----Original Message-----
> >>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-
> >>On Behalf Of Tzlil Shushan
> >>Sent: Wednesday, 05 August, 2020 5:45
> >>To: r-sig-meta-analysis using 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
> >>My first question is around the methods used to assign weights within
> >>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. I
> >>found this method in a lecture by Joshua R. Polanin
> >>https://www.youtube.com/watch?v=rJjeRRf23L8&t=1719s from 28:00.
> >>W = 1/vi, then divided by the number of ES for a study
> >>for example, a study with vi = 0.0402 and 2 different ES will weight as
> >>1/0.0402 = 24.88, then 24.88/2 = 12.44 (finally, converting into
> >>percentages based on the overall weights in the analysis)
> >>After I've read some of the great posts provided in last threads here
> >>I wonder if it is not correct and I need to modify the way I use weights
> >>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
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