[R-meta] metafor - averaging over rma result

[Viechtbauer, Wolfgang (SP)]

Dear Stephanie,

My suggestion would be to fit a sensible model that uses 'disorder' as a predictor and then plot the model coefficients using the forest() function. Here is an example, not using VRs, but the idea is the same:

library(metafor)

dat <- dat.bourassa1996

# calculate log(OR) and corresponding sampling variances
dat <- escalc(measure="OR", ai=lh.le, bi=lh.re, ci=rh.le, di=rh.re, data=dat)
dat

# fit model
res <- rma(yi, vi, mods = ~ eye_assess - 1, data=dat, subset=sex=="combined")
res

# by removing the intercept, the coefficients are the estimated log(OR)s for each level of 'eye_assess'

# pass coefficients and variance of the coefficients to forest() function
forest(coef(res), diag(vcov(res)), slab=names(coef(res)), atrans=exp, 
       at = c(log(c(1, 2, 4, 8, 16, 32))), xlab="Odds Ratio (log scale)")

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On Behalf Of stephanie.winkelbeiner using bli.uzh.ch
Sent: Tuesday, 22 October, 2019 15:44
To: r-sig-meta-analysis using r-project.org
Subject: [R-meta] metafor - averaging over rma result

Dear experts, 

for a meta-analysis, we calculated the variability ratio (VR) of all included studies
using the rma function of the metafor package.

We would like to plot the results in a forest plot grouped by disorder for a less 
busy presentation (compared to plotting the VR for every study).

Yet, averaging over the VRs, especially over the confidence intervals seems
worrying.

Do you have any suggestions?

Thank you, 
Stephanie