# [R-meta] Separate tau for each subgroup in mixed-effect models

Arthurcsirio @rthurc@|r|o @end|ng |rom gm@||@com
Thu Sep 1 12:02:15 CEST 2022

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This is great.

Thanks, Wolfgang.

Arthur Albuquerque

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> On Sep 1, 2022 at 5:18 AM,  <Wolfgang (NP) Viechtbauer (mailto:wolfgang.viechtbauer using maastrichtuniversity.nl)>  wrote:
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>  This has also been possible with the rma.mv() function: https://www.metafor-project.org/doku.php/tips:comp_two_independent_estimates#meta-regression_with_all_studies_but_different_amounts_of_residual_heterogeneity So, actually, there are three different ways one can do this: 1) Fit separate RE models within subgroups. dat  <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg) res1  <- list(rma(yi, vi, data=dat, subset=alloc=="alternate"), rma(yi, vi, data=dat, subset=alloc=="random"), rma(yi, vi, data=dat, subset=alloc=="systematic")) dat.comp  <- data.frame(meta = c("alternate","random","systematic"), estimate = sapply(res1, coef), stderror = sqrt(sapply(res1, vcov)), tau2 = sapply(res1, \(x) x\$tau2)) dat.comp  <- dfround(dat.comp, 4) dat.comp 2) Fit an rma.mv() model with a random effects structure that allows tau^2 to differ across groups. res2  <- rma.mv(yi, vi, mods = ~ 0 + alloc, random = ~ alloc | trial, struct="DIAG", data=dat) res2 3) Use a location-scale model with a categorical scale variable. res3  <- rma(yi, vi, mods = ~ 0 + alloc, scale = ~ 0 + alloc, data=dat) res3 predict(res3, newscale=diag(3), transf=exp) Instead of using the (default) log link, we can also use an identity link to fit this model: res4  <- rma(yi, vi, mods = ~ 0 + alloc, scale = ~ 0 + alloc, data=dat, link="identity") res4 Compare the log likelihoods: sum(sapply(res1, logLik)) # add up the three log likelihoods logLik(res2) logLik(res3) logLik(res4) The results match up nicely, as they should.[1] This is in fact a nice confirmation that the underlying code - which is rather different for these different approaches - works as intended. [1] You might actually see minor discrepancies here and there. They can arise due to differences in how these models are fitted and the optimization routines used. Best, Wolfgang  >-----Original Message-----  >From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On  >Behalf Of James Pustejovsky  >Sent: Thursday, 01 September, 2022 2:06  >To: Arthur Albuquerque  >Cc: R meta  >Subject: Re: [R-meta] Separate tau for each subgroup in mixed-effect models  >   >Hi Arthur,  >   >Yes, these sorts of models are now supported in metafor::rma.uni(). For  >details, see  >https://wviechtb.github.io/metafor/reference/rma.uni.html#location-scale-models  >For a sub-group analysis with a categorical moderator called `mod`, the  >syntax would look something like  >rma.uni(yi = yi, sei = sei, mods = ~ mod, scale = ~ mod, data = dat, method  >= "REML")  >   >Best,  >James  >   >On Wed, Aug 31, 2022 at 6:43 PM Arthur Albuquerque  <arthurcsirio using gmail.com>   >wrote:  >   >>  Hi all,  >>   >>  I plan to fit a mixed-effects meta-regression model with metafor::rma().  >>  The moderator would be categorical (3 subgroups). If I’m not mistaken,  >>  rma() estimates a common tau^2 across subgroups.  >>   >>  Is it possible to estimate a separate tau for each subgroup?  >>   >>  I believe it is possible in the {meta} package through the tau.common  >>  argument (https://cran.r-project.org/web/packages/meta/meta.pdf).  >>   >>  Thanks,  >>   >>  Arthur M. Albuquerque  >>   >>  Medical student  >>  Universidade Federal do Rio de Janeiro, Brazil
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