[R-meta] Question: Metafor R package - rma and rma.mv function

Jens Schüler jen@@@chueler @ending from wiwi@uni-kl@de
Wed Jul 25 11:28:01 CEST 2018

Hi Simona,

I am not sure whether it is applicable to your study/problem but I had to
deal with a similar situation and ended up with nesting multiple effects
within studies.
See Wolfgang's reply:


-----Ursprüngliche Nachricht-----
Von: R-sig-meta-analysis <r-sig-meta-analysis-bounces using r-project.org> Im
Auftrag von Simona Frederiksen
Gesendet: Mittwoch, 25. Juli 2018 11:08
An: r-sig-meta-analysis using r-project.org
Betreff: [R-meta] Question: Metafor R package - rma and rma.mv function


I have some question sthat I would like to get posted regarding the metafor
R package and the rma and rma.mv function. It goes like this:


I am at the moment working in the R package 'metafor' in order to perform a
meta-analysis which I carry out in collaboration with the Danish Headache
Center. I just figured that I have to use the rma.mv function since I have
several effect sizes per study (for two studies).

I have following questions that I hope you can help me answer:

  1.  When I calculate the weights for each study, the studies that have
more effect sizes receive a really high weight compared to the other studies
with just one effect size. So it seems that these studies primarily are used
to calculate the overall effect size even though N seems to be quite small?

  1.  How can I see convergence when I use the rma.mv function? When using
the rma function, it turns up when adding verbose = T. And if it does not
converge, what would be ideal to do?

Here is an example:
study_id <- c(1,2,3,3)
authors <- c("Authors1", "Authors2", "Authors3", "Authors3") year <- c(2013,
2009, 1994, 1994) Case_N <- c(18,25,4,4) Case_mean <- c(53, 236, 65, 64)
Case_sd <- c(31, 216, 19, 18) Control_N <-c(18,50,4,4) Control_mean <-
c(39,113,67,68) Control_sd <- c(27, 219,10,10) dat2A <-
data.frame(study_id,authors, year, Case_N, Case_mean, Case_sd,
               Control_N, Control_mean, Control_sd)

dat1 <- escalc(measure="ROM", data=dat2A,
               m1i = Case_mean,
               n1i = Case_N,
               sd1i = Case_sd,
               m2i = Control_mean,
               n2i = Control_N,
               sd2i = Control_sd,
               slab=paste(authors, year, sep=", "))

res <- rma.mv(yi, vi, data=dat1,random = ~ 1 | study_id, verbose=T)

Can you help me to understand this?

Best wishes,
Simona D Frederiksen

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