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

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)