[R-meta] Meta-analysis per level or meta-regression

[Catia Oliveira]

Thank you so much James and Wolfgang! This is extremely helpful!
I wish you a lovely week.

Best wishes,

Catia

On Mon, 20 Mar 2023 at 19:24, Viechtbauer, Wolfgang (NP) via
R-sig-meta-analysis <r-sig-meta-analysis using r-project.org> wrote:

> Just to add to this; these two pages on the metafor website are relevant
> to this discussion:
>
>
> https://www.metafor-project.org/doku.php/tips:comp_two_independent_estimates
>
> https://www.metafor-project.org/doku.php/tips:different_tau2_across_subgroups
>
> Best,
> Wolfgang
>
> >-----Original Message-----
> >From: R-sig-meta-analysis [mailto:
> r-sig-meta-analysis-bounces using r-project.org] On
> >Behalf Of James Pustejovsky via R-sig-meta-analysis
> >Sent: Monday, 20 March, 2023 19:27
> >To: R Special Interest Group for Meta-Analysis
> >Cc: James Pustejovsky
> >Subject: Re: [R-meta] Meta-analysis per level or meta-regression
> >
> >Hi Catia,
> >
> >I don't know of research that has looked at differences between these
> >approaches empirically.
> >
> >I would interpret the issue in terms of a difference between two
> >meta-regression models: one in which the between-study heterogeneity is
> >constrained to be equal across levels of the moderator and one in which
> the
> >between-study heterogeneity is allowed to differ by level of the
> moderator.
> >María Rubio-Aparicio and colleagues compared these two models in a
> >simulation study:
> >https://doi.org/10.1080/00220973.2018.1561404
> >
> >It's also now possible to fit and compare both models using metafor:
> >res_hom <- rma(yi, vi, mods = ~ alloc, data=dat)
> >res_het <- rma(yi, vi, mods = ~ alloc, scale = ~ alloc, data=dat)
> >anova(res_het, res_hom) # Likelihood ratio test and model fit statistics
> >
> >Some analysts would simply fit both models and justify
> >their preferred model based on the fit statistics. Others might argue that
> >it's preferable to always use the more flexible model for purposes of
> >testing moderators; see Rodriguez et al. (2023;
> >https://doi.org/10.1111/bmsp.12299).
> >
> >James
> >
> >On Mon, Mar 20, 2023 at 1:04 PM Catia Oliveira via R-sig-meta-analysis <
> >r-sig-meta-analysis using r-project.org> wrote:
> >
> >> Dear all,
> >>
> >> Does anyone know of a manuscript that has compared the effect sizes when
> >> running separate meta-analyses per level of a variable of interest
> against
> >> those of running a meta-regression where we remove the intercept?
> >>
> >> e.g.,
> >>
> >> ### mixed-effects meta-regression model with categorical moderator
> >> res <- rma(yi, vi, mods = ~ alloc, data=dat)
> >> res
> >>
> >> You will find:
> >>
> >> Test of Moderators (coefficients 2:3):
> >> QM(df = 2) = 1.7675, p-val = 0.4132
> >>
> >> Model Results:
> >>
> >>                  estimate      se     zval    pval    ci.lb   ci.ub
> >> intrcpt           -0.5180  0.4412  -1.1740  0.2404  -1.3827  0.3468
> >> allocrandom       -0.4478  0.5158  -0.8682  0.3853  -1.4588  0.5632
> >> allocsystematic    0.0890  0.5600   0.1590  0.8737  -1.0086  1.1867
> >>
> >>
> >> Instead of doing this, we could also run one meta-analysis for
> allocrandom
> >> and another for allocsystematic.
> >> I know the results will be similar, I just need to have something that
> >> proves this beyond running the model and presenting the findings. Also,
> >> meta-regression allows us to compare the different levels, which is the
> >> point. I don't understand why we are questioned about this when running
> a
> >> meta-regression but if this was a linear regression using this approach
> >> would be standard.
> >>
> >> Best wishes,
> >>
> >> Catia
> >>
> >> --
> >> Cátia Margarida Ferreira de Oliveira
> >> Research Associate
> >> Department of Psychology, Room C222
> >> University of York, YO10 5DD
> >> Twitter: @CatiaMOliveira
> >> pronouns: she, her
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