[R-meta] (no subject)

Dear Garance,

Please see my responses below.

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:

r-sig-meta-analysis-bounces at r-project.org] On

Behalf Of Garance Delagneau
Sent: Friday, 18 June, 2021 2:53
To: r-sig-meta-analysis at 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,
--
GARANCE DELAGNEAU
PhD Student (Clinical Neuropsychology)

M: 0452 323 762
E: garance.delagneau at monash.edu