[R-meta] rma.mv meta-regression

[Simon Harmel]

Dear James,

If I may ask three quick follow-ups:

(1) Why are you removing the intercept (~ 0) in your rma.mv() call?
(2) What if, only a small portion of studies have used multiple outcomes?
Is a multivariate multilevel model still recommended?
(3) What if, multiple-end point and multi-treatment studies co-occur in the
pool of studies? Should RVE be preferred over a multivariate multilevel
model?

Thank you, for your consideration,
Simon

On Mon, Jan 4, 2021 at 3:35 PM James Pustejovsky <jepusto using gmail.com> wrote:

> Hi Emanuele,
>
> Comments inline below.
>
> Kind Regards,
> James
>
> On Mon, Jan 4, 2021 at 10:25 AM Emanuele F. Osimo <efo22 using 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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>
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