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

Brendan Pearl brend@n @end|ng |rom brend@n@-b|t@@com
Fri Sep 29 23:26:19 CEST 2023 

Thankyou both.

------- Original Message -------
On Saturday, September 30th, 2023 at 3:04 AM, Reza Norouzian via R-sig-meta-analysis <r-sig-meta-analysis using r-project.org> wrote:


> 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 using 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 using 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 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)
> > 
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