[R-meta] rma.mv meta-regression

Hi Emanuele,

Comments inline below.

Kind Regards,
James

On Mon, Jan 4, 2021 at 10:25 AM Emanuele F. Osimo <efo22 at cam.ac.uk> wrote:

Dear all,
as usual, apologies for a potentially silly question.

I am doing multi-variate meta-analysis of studies looking a different
inflammatory markers (called cytokines, in short cyto).
Each study measured multiple cytokines for the same sample.

The code is running like so:

rma.mv(yi=yi,V=vi, mods=~cyto-1, random = ~cyto|studycode,

struct="UN",

method='REML', data = mydata, control=list(optimizer="hjk"))

Are the measures of different cytokines correlated? Is it possible to get
estimates of the degree of correlation between the outcomes in each study?
If so, then it would be preferable to specify a true multivariate model
that allows for correlation between the effect size estimates themselves
(i.e., in the V matrix). Example code here:
http://www.metafor-project.org/doku.php/analyses:gleser2009
If it is not possible to get the correlations between outcomes, then it
might be advisable to still make a guess about the degree of correlation,
as demonstrated here:

https://www.jepusto.com/imputing-covariance-matrices-for-multi-variate-meta-analysis/

I was wondering if it is possible to perform meta-regression using the
same technique, such as on average study participant age (a variable
called age), on all studies at the same time, but grouped by cyto, and
what the code would look like.

Do you mean that you want to allow the relationship between the moderator

and effect size to be different for each type of cytokine? If so, then you
can specify this using an interaction between cyto and the moderator:

  > rma.mv(yi = yi, V = vi, mods = ~ 0 + cyto + cyto:age, random = ~cyto |

studycode, struct = "UN", method = 'REML', data = mydata, control =
list(optimizer = "hjk"))

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