OP edited to correct for Wolfgang's comments...
I am conducting a meta-analysis of outcome data and am using the metafor package with the recommended logit transformation see related post. My question is about the output. It feels to me like the weights are really off and I don't know what I'm misunderstanding.
For, example, in the following data file (with study source, ni, xi and then the up percentages for illustration only), it's easy to see that the bulk of the results suggest success in less than 50% of the cases because Studies 5-7 have such large sample size.
Study ni xi up
1 8 7 87.5
1 10 10 100
1 11 11 100
2 12 10 83.3
3 13 12 92.3
4 14 12 85.7
4 14 14 100
5 132 55 41.3
6 141 63 44.68
6 141 57 40.43
7 220 80 36.2
7 220 84 38
I used the following commands in metafor
selection2 <- escalc(measure="PLO", xi=xi, ni = ni, data=selection2)
overall2 <- rma.mv(yi, vi, random = ~1 | Study/Group, data = selection2)
predict(overall2, transf=transf.ilogit)
The output for the second part is
pred ci.lb ci.ub cr.lb cr.ub <br>
0.5532 0.3767 1.4685 0.1420 -0.1852 1.2916
I take this to mean that the data suggest an overall success rate of 55%, even though not one of the larger studies shows that effect. Intuitively, this seems to not match my actual data.
Have I made an error in analysis? In interpretation?
Study
and for each row in the dataset. Also, the output in your post does not match these data. $\endgroup$rma.glmm(measure="PLO", xi=xi, ni=ni, data=selection2)
. $\endgroup$