[R-meta] metafor - averaging over rma result
Viechtbauer, Wolfgang (SP)
wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Tue Oct 22 17:02:58 CEST 2019
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:
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)
# fit model
res <- rma(yi, vi, mods = ~ eye_assess - 1, data=dat, subset=sex=="combined")
# 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)")
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
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
Do you have any suggestions?
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