[R-meta] (no subject)

Garance Delagneau g@r@nce@de|@gne@u @end|ng |rom mon@@h@edu
Fri Jun 18 15:29:07 CEST 2021

Dear Wolfgang,

Sorry for the lack of information and thank you so much for the detailed
response I really appreciate it. I am indeed using the aggregate function
I’ll make sure to double check my calculation using the method
outlined in Borenstein's
manual but your explanation made complete sense.

I am currently separately analyzing studies which reported various effect
sizes and then combining them all once a composite effect size is being
calculated. Is that what you would suggest doing? I’m also wondering what
you would suggest to do for studies which reported effect sizes for both
different measures and timepoints (eg 2 effect sizes at time point 1 and
two others at time point 2).

Thank you very much
Kind regards

Le ven. 18 juin 2021 à 18:40, Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> a écrit :

> Dear Garance,
> Please see my responses below.
> Best,
> Wolfgang
> >-----Original Message-----
> >From: R-sig-meta-analysis [mailto:
> r-sig-meta-analysis-bounces using r-project.org] On
> >Behalf Of Garance Delagneau
> >Sent: Friday, 18 June, 2021 2:53
> >To: r-sig-meta-analysis using r-project.org
> >Subject: [R-meta] (no subject)
> >
> >Hi everyone,
> >
> >I'm a bit stuck and would really appreciate any help on my issue.
> >
> >I'm doing a meta analysis (using R). There are several instances where
> authors
> >reported multiple effect sizes (e.g., reported effect sizes for different
> >timepoints) that I need to combine. I've tried to aggregate my multiple
> effect
> >sizes using both the metafor package and the formula in Borenstein's
> manual
> >(chapter 24 - using the mean effect size weighted according to the sample
> size and
> >the formula attached to this email to calculate the variance).
> The equation you showed assumes that an *unweighted* average is taken of
> the two effect sizes. So if you computed a weighted mean, then this
> equation is not correct.
> >While variances
> >using these two techniques are quite similar, the computed effect sizes
> are very
> >different.
> >
> >My questions are:
> >• Why/how does yi (combined effect size) change quite a lot based on the
> value of
> >rho when using the metafor package?
> I assume you are talking about the aggregate() function and you are using
> something like:
> aggregate(dat, cluster=dat$study, struct="CS", rho=<>)
> The function by default computes weighted averages of the effects within
> studies (based on the variance-covariance matrix of the effects, which is
> constructed based on the sampling variances and the assumed value of rho).
> When rho changes, the var-cov matrix changes and hence the weighted
> averages change. You can also use weighted=FALSE in which case unweighted
> averages are computed and then rho does not affect these averages (although
> it still affects the variances of the computed averages).
> >• Are the yi's that we get when using the metafor package correct?
> I think so, but my opinion on this matter might be biased :) You can
> inspect the code of the function here:
> https://github.com/wviechtb/metafor/blob/master/R/aggregate.escalc.r If
> you find any mistakes/errors, please let me know!
> >• The combined effect sizes using these methods are quite different from
> using the
> >mean effect size. Is it correct to use the Metafor package?
> >This is the example I've been working on
> >
> >Authors        N   Time  corr
> >Polanska  2017 337 2     -0.09
> >Polanska  2017 219 1     -0.02
> >
> >Using R's metafor package, I obtained a combined effect size of  -0.0718.
> Using
> >Borenstein's method, I obtain an effect size of -0.06255.
> Please provide a fully reproducible example. I had to guess what exactly
> you did with metafor, but it might have been this:
> library(metafor)
> dat <- data.frame(study=1, ni=c(337,219), ri=c(-.09,-.02))
> dat <- escalc(measure="COR", ri=ri, ni=ni, data=dat)
> aggregate(dat, cluster=dat$study, struct="CS", rho=0.555)
> At least this yields yi=-0.0718.
> aggregate(dat, cluster=dat$study, struct="CS", rho=0.555, weighted=FALSE)
> gives an unweighted average of -0.0550 (following Borenstein). Not sure
> what you did but
> weighted.mean(dat$ri, dat$ni)
> gives -0.06242806 which is close to -0.06255 but not identical (and again
> this is not what Borenstein suggests).
> >Note. I often have fewer than 10 articles to combine in my meta-analyses
> (it
> >varies between 3 and 10). I expect heterogeneity to be moderate to high
> in most of
> >my analyses.
> >
> >Thank you very much,
> >--
> >PhD Student (Clinical Neuropsychology)
> >
> >M: 0452 323 762
> >E: garance.delagneau using monash.edu
PhD Student (Clinical Neuropsychology)

M: 0452 323 762
E: g <elinor.fraser using monash.edu>arance.delagneau using monash.edu

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