[R-meta] Aggregating dependent effect sizes for trimfill

[Andreas Voldstad]

Dear Wolfgang, James and all,

I am doing a multilevel meta-analysis of SMDs, with partially empirical correlated and hierarchical effects ("PECHE"), corrected with cluster-robust variance estimation.

For assessment of publication bias risk, I have done Egger's regression by standardising the effect sizes and adding the inverse of their standard error as a moderator.

I would like to add some of the methods that are not compatible with dependent effect sizes, such as trim and fill, rank correlation test and perhaps stepwise models.

For visualisation, I have already aggregated the data based on this post: https://www.metafor-project.org/doku.php/tips:forest_plot_with_aggregated_values

And confirmed that running the rma.uni with REML on the aggregated data, and then applying RVE, yields practically the same results to the original multilevel model (i.e., up to .01 difference in the 95% CI).

I am wondering what you think in general about applying methods not suitable for rma.mv models, such as trimfill and ranktest, to this aggregated data (and the corresponding aggregated funnel plot)?

I performed rma.uni on the aggregated data, and passed it on to trimfill to get k0, a filled funnel plot, and a corrected effect.

If this is a valid approach, I am also wondering if there is a way to apply robust() to the trimfill corrected effect, so that it will be comparable to the effect from my original analysis?


Best wishes,

Andreas Voldstad (he/him)
PhD student in Psychiatry
University of Oxford
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