[R-meta] Multilevel meta-analysis with a categorical moderator | subgroup analysis using meta-regression

[Reza Norouzian]

Hi Katharina,

Yes, for the type of model you're using, it's possible to use a single
model to conduct a subgroup analysis.

See for example:
https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2022-June/004074.html

Reza


On Tue, Apr 16, 2024, 3:25 AM Katharina Agethen via R-sig-meta-analysis <
r-sig-meta-analysis using r-project.org> wrote:

> Dear all,
>
> I'm currently working on a meta-analysis on collective orientation and job
> performance. I'm conducting a multilevel meta-analysis to account for
> dependency in the data because multiple predictors (i.e., several measures
> of collective orientation) and multiple outcomes (i.e., several measures of
> job performance (e.g., task performance, contextual performance)) were
> often assessed in the same sample. Correlation values were converted to
> Fisher's z scale.
>
> The code for the main effect is as follows:
>
> full.model <- rma.mv(yi = z,
>                      V = vz,
>                      slab = samid,
>                      data = df,
>                      random = ~ 1 | samid/esid,
>                      test = "t",
>                      method = "REML",
>                      dfs="contain")
>
> summary(full.model)
>
> full.model.robust <- robust(full.model, cluster=df$samid, clubSandwich =
> TRUE)
> summary(full.model.robust)
>
>
> In addition, I want to test the type of performance (pertype) as a
> categorical moderator (i.e., general, in-role, extra-role). I fitted a
> meta-regression model with pertype as the categorical moderator based on
> all studies:
>
> mod.pertype <- rma.mv(yi = z,
>                     V = vz,
>                     slab = samid,
>                     data = df,
>                     random = ~ 1 | samid/esid,
>                     test = "t",
>                     method = "REML",
>                     dfs="contain",
>                     mods = ~ pertype)
>
> summary(mod.pertype)
>
> mod.pertype.robust <- robust(mod.pertype, cluster=df$samid, clubSandwich =
> TRUE)
> summary(mod.pertype.robust)
>
> Am I right that, in this case, the amount of residual heterogeneity will
> be the same in each subgroup?
> Is it possible to fit a multilevel model with the subgroups using
> meta-regression while allowing the amount of residual heterogeneity to vary
> across subgroups?
>
> I understand that I could fit three separate multilevel models for each
> subgroup and then compare the estimates using a Wald-type test. But I'm
> wondering whether I can fit a single model with varying heterogeneity
> across subgroups?
> I read Wolfgang's examples of how to compare estimates from independent
> meta-analyses and subgroups (
> http://www.metafor-project.org/doku.php/tips:comp_two_independent_estimates).
> But I'm not sure how to apply these examples to a multilevel meta-analysis
> with categorical moderators.
>
> Thanks a lot for your help.
>
> Best,
> Katharina
>
> --
> Katharina Agethen
> Research Assistant
> Human Resource Management
>
> OWL University of Applied Sciences and Arts
> Department of Business Administration and Economics
> Campusallee 12
> 32657 Lemgo, Germany
>
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