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