[R-meta] Difference between subset (in a loop) and mods in metafor rma.mv

Also, Brenden, if you want to replicate the results from when you subset
the data (i.e., not allowing the borrowing of info.) with a single model,
then you can do:

rma.mv(yi~Predictor+0, vi, random = list(~Predictor | Study, ~Predictor |
Article),
struct = c("DIAG","DIAG"), data=dat)

which for your toy dataset, where predictor's categories have no
heterogeneity at the Article level, may be reduced to:

rma.mv(yi~Predictor+0, vi, random = ~Predictor | Study,
struct = "DIAG", data=dat)

This topic has come up on the list a number of times, you may want to scan
through the listserv's archives for related examples (e.g.,
https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2022-June/004074.html).

Reza


On Fri, Sep 29, 2023 at 11:25?AM Viechtbauer, Wolfgang (NP) via
R-sig-meta-analysis r-sig-meta-analysis at r-project.org wrote:

Yes. For some relevant literature, see for example:

Riley, R. D., Abrams, K. R., Lambert, P. C., Sutton, A. J., & Thompson, J.
R. (2007). An evaluation of bivariate random-effects meta-analysis for the
joint synthesis of two correlated outcomes. Statistics in Medicine, 26(1),
78-97. https://doi.org/10.1002/sim.2524

Jackson, D., White, I. R., Price, M., Copas, J., & Riley, R. D. (2017).
Borrowing of strength and study weights in multivariate and network
meta-analysis. Statistical Methods in Medical Research, 26(6), 2853-2868.
https://doi.org/10.1177/0962280215611702

Copas, J. B., Jackson, D., White, I. R., & Riley, R. D. (2018). The role
of secondary outcomes in multivariate meta-analysis. Journal of the Royal
Statistical Society, Series C, 67(5), 1177-1205.
https://doi.org/10.1111/rssc.12274

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:
r-sig-meta-analysis-bounces at r-project.org] On
Behalf Of Brendan Pearl via R-sig-meta-analysis
Sent: Friday, 29 September, 2023 13:56
To: R Special Interest Group for Meta-Analysis
Cc: Brendan Pearl
Subject: Re: [R-meta] Difference between subset (in a loop) and mods in
metafor
rma.mv

Thankyou Wolfgang,

Just for my understanding: "When using the full dataset and using
'Predictor' as
a moderator, then it is possible for information to be shared/borrowed
from both
levels of the moderator within studies." - is this what occurs in a
multivariable/multivariate meta-analysis?

Regards,
Brendan.

------- Original Message -------
On Friday, September 29th, 2023 at 8:56 PM, Viechtbauer, Wolfgang (NP)
wolfgang.viechtbauer at maastrichtuniversity.nl wrote:

Dear Brendan,

Both approaches fit multilevel models. The difference arises because of
two
reasons:

1) When using the full dataset and using 'Predictor' as a moderator,
then it is
possible for information to be shared/borrowed from both levels of the
moderator
within studies.

2) When subsetting based on 'Predictor', you allow the variance
components to
differ across the two levels of the moderator. This is essentially what is
discussed here:

https://www.metafor-project.org/doku.php/tips:comp_two_independent_estimates

but in the context of a simpler model.

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:
r-sig-meta-analysis-bounces at r-project.org]
On
Behalf Of Brendan Pearl via R-sig-meta-analysis
Sent: Friday, 29 September, 2023 12:00
To: r-sig-meta-analysis at r-project.org
Cc: Brendan Pearl
Subject: [R-meta] Difference between subset (in a loop) and mods in
metafor
rma.mv

I am trying to understand the difference using the 'subset' and 'mods'
options in
rma.mv.

I have run two analyses using rma.mv on the same dataset that has a
three
level
structure (articles nested within larger studies) and get different
results.

In the first example I have given below, is looping over rma.mv and
passing a
different predictor to the subset option a univariate meta-analysis?
And in
the
second example, is passing the predictor to the mods option running a
multivariable meta-analysis?

Study <- c("A","A","B","C","C","D","E","F","F","G")
Article <- c("1","2","3","4","5","6","7","8","9","10")
Predictor <- c("x","x","x","y","y","y","x","x","y","y")
yi <- c(-.2,-.3,-.8,.5,.6,.4,-.1,-.8,.3,.8)
vi <- c(.01,.01,.01,.01,.01,.01,.01,.01,.01,.01)

dat <- data.frame(Study,Article,Predictor,yi,vi)

#FIRST EXAMPLE#Subset analyses
# gives me

#1 x OR = -0.49 [-0.84, -0.13]
#2 y OR = 0.51 [0.31, 0.72]

df_subset <-
data.frame(val=as.character(),subset_result=as.character())

for (val in unique(dat$Predictor)){

res_univariate <- rma.mv(yi=yi,
V=vi,
data=dat,
random = ~1 | Study/Article,
subset = Predictor == val,
method = "REML"
)

result <- capture.output(cat(
"OR = ",
format(round(res_univariate$beta, 2),nsmall = 2),
" [",
format(round(res_univariate$ci.lb, 2),nsmall = 2),
", ",
format(round(res_univariate$ci.ub, 2),nsmall = 2),
"]",
sep=""
)
)

df_subset[nrow(df_subset) +1,] = c(
val,
result
)

}

#SECOND EXAMPLE
#Mod analysis
#gives me:

# estimate ci.lb ci.ub

#factor(Predictor)x -0.4825 -0.7246 -0.2404
#factor(Predictor)y 0.5807 0.3386 0.8229

df_mod <- data.frame(val=as.character(),mod_result=as.character())

res_multivariate <- rma.mv(yi=yi,
V=vi,
data=dat,
random = ~1 | Study/Article,
mods = ~ factor(Predictor)-1,
method = "REML"
)
summary(res_multivariate)

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