[R-meta] Subgroup analysis output using metafor - interpretation

Michael Dewey ||@t@ @end|ng |rom dewey@myzen@co@uk
Thu Jan 9 14:57:44 CET 2020

Dear Joao

I hink we may need some clarification before we can answer this. 
Comments in-line below

On 09/01/2020 13:36, Joao Afonso wrote:
> Dear all,
> I am running a meta-analysis on the prevalence of lameness (binary) in
> British dairy cattle and have used the *metaprop* from the *metafor* package.

I think metaprop comes from meta, not metafor?

> I have set the model to run with random effects, using the DL method, and
> have taken the following approach:
>     1. log-transform the data as it is not normally distributed

If it is binary data I would not have expected that anyway so what 
exactly did you transform?

>     2. identify outliers using influential analysis (only ran this once)
>     3. remove outliers and rerun the model

In general that seems a bad idea as it removes the most interesting 
observations but you may have reasons to doubt the observations.

>     4. deal with remaining heterogeneity with subgroup analysis and
>     meta-regression
> I have ran the model and am getting what I believe conflicting evidences on
> different output indicators. As an example, after running subgroup analysis
> with one moderator, the output tells me that the moderator explains around
> 50% of the heterogeneity (R^2), and yet the p-value for the test of
> moderators is substantially higher than 0.05 telling me that the pooled
> estimates of the subgroups aren't actually different.

Can you share the output from the analysis to give us a clue?

> I was hoping you could shed a light as to what could justify this happening
> (if it makes sense), and possibly provide some guidance as to what I could
> do to improve the statistical evidences of my study.
> Many thanks and happy 2020 to everyone


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