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

[Brendan Pearl]

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 using 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 using 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 using 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)