[R-meta] Testing for subgroup differences - meta (byvar) vs metafor

Hi Thomas,

The results are the same, but you just have to go about things in
different ways to get the same results. Start with:

metagen(MD, seMD, byvar = region, data = Fleiss93cont, tau.common = TRUE,
        method.tau = "REML", sm = "MD")
rma(y = MD, sei = seMD, mods = ~ region - 1, data = Fleiss93cont,
    method = "REML")

So, assuming a common tau^2 value across subgroups and not using the K&H
adjustment. By using 'mods = ~ region - 1' you get the estimate for each
level of 'region'. If you leave out the - 1, then one level (in this case
'Asia') becomes the reference level and the coefficient for 'Europe' is the
difference between the two levels.

Now let's use the K&H / H&K adjustment:

metagen(MD, seMD, byvar = region, data = Fleiss93cont, tau.common = TRUE,
        method.tau = "REML", sm = "MD", hakn = TRUE)
rma(y = MD, sei = seMD, mods = ~ region - 1, data = Fleiss93cont,
    method = "REML", test = "knha")

You will again get the same estimates, but the CIs are different.
metagen() estimates a common tau^2 value and then applies the K&H
adjustment separately within each subset. rma() also estimates a common
tau^2 value but then applies the meta-regression version of the K&H
adjustment. This will give different results. To get the same results, use:

res <- rma(y = MD, sei = seMD, mods = ~ region - 1, data = Fleiss93cont,
           method = "REML", test = "knha")
rma(y = MD, sei = seMD, data = Fleiss93cont, method = "REML", test =
"knha",
    subset=region=="Asia", tau2 = res$tau2)
rma(y = MD, sei = seMD, data = Fleiss93cont, method = "REML", test =
"knha",
    subset=region=="Europe", tau2 = res$tau2)

So, first estimate the common tau^2 value and then fit separate RE models
in each subset with the K&H adjustment and specify the value of the tau^2
from the first step.

Now let's do this without assuming a common tau^2 value:

metagen(MD, seMD, byvar = region, data = Fleiss93cont, tau.common = FALSE,
        method.tau = "REML", sm = "MD")
rma.mv(MD, V= seMD^2, mods = ~ region - 1, method = "REML", random =
~region|study,
       struct="DIAG", data=Fleiss93cont)

As you show, here one can use rma.mv() to accomplish the same thing and
the results are the same. And now with K&H adjustment:

metagen(MD, seMD, byvar = region, data = Fleiss93cont, tau.common = FALSE,
        method.tau = "REML", sm = "MD", hakn = TRUE)
rma(y = MD, sei = seMD, data = Fleiss93cont, method = "REML", test =
"knha",
    subset=region=="Asia")
rma(y = MD, sei = seMD, data = Fleiss93cont, method = "REML", test =
"knha",
    subset=region=="Europe")

So, for rma(), just fit RE models in each subset using the K&H adjustment
and then the results are the same. And tau^2 is then of course estimated
separately within each subset.

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-
bounces at r-project.org] On Behalf Of Zelniker, Thomas
Sent: Wednesday, 06 December, 2017 3:08
To: r-sig-meta-analysis at r-project.org
Subject: [R-meta] Testing for subgroup differences - meta (byvar) vs
metafor

Dear all,

I am performing a meta-analysis of e.g. 4 trials. Each of these trials
reports results for 2 subgroups (group A and group B). My aim is to
meta-analyze and test for interaction between these two subgroups.
The R package meta allows doing this very easily by including the term
byvar. However, I am not able to replicate this with the package metafor.
Could anyone explain to me how the byvar statement works?

What would be the best approach?

Please find below an example indicating the differences between metafor
and meta:

library(meta)
library(metafor)
rm(list = ls())
data(Fleiss93cont)
# Add some (fictious) grouping variables:
Fleiss93cont$age <- c(55, 65, 55, 65, 55)
Fleiss93cont$region <- c("Europe", "Europe", "Asia", "Asia", "Europe")
Fleiss93cont$MD <- with(Fleiss93cont, mean.e - mean.c)
Fleiss93cont$seMD <- with(Fleiss93cont, sqrt(sd.e^2/n.e + sd.c^2/n.c))

meta_mod <- metagen(MD, seMD,
                    byvar = region,
                    data = Fleiss93cont,
                    tau.common = F,
                    method.tau = "REML",
                    hakn = T,
                    sm = "MD")

# Here the results:
meta_mod
rma(y = MD, sei = seMD, mods = ~ region, data = Fleiss93cont, method =
"REML", test = "knha", intercept = T)
rma.mv(MD, V= seMD^2, mods = ~ region, method = "REML",  test="t", random
= ~region|study, struct="DIAG", data=Fleiss93cont)

Best regards,
Thomas