On Sep 1, 2022 at 5:18 AM, <Wolfgang (NP) Viechtbauer (mailto:wolfgang.viechtbauer at maastrichtuniversity.nl)> wrote:
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 at 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 at 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