[R-meta] fixed-effect multivariate model interpretation

Filippo Gambarota ||||ppo@g@mb@rot@ @end|ng |rom gm@||@com
Mon Jan 3 18:02:17 CET 2022

Ah great! do you have any references for this? because I would like to
clearly understand what is going on. Cochrane's Q-statistic is the
deviation of each study from the estimated average effect weighted by the
precision. In this case, the "average" effect is the average between
outcomes? Or the formula is different?
Thank you!

On Mon, 3 Jan 2022 at 17:12, Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:

> It's just the multivariate version of Cochrane's Q-test. It does not
> estimate a random-effects model. It simply tests whether the observed
> amount of variability is larger than expected based on the sampling
> variances (and their covariances when V includes those) and any moderators
> specified.
> Best,
> Wolfgang
> >-----Original Message-----
> >From: Filippo Gambarota [mailto:filippo.gambarota using gmail.com]
> >Sent: Monday, 03 January, 2022 17:11
> >To: Viechtbauer, Wolfgang (SP)
> >Cc: R meta
> >Subject: Re: [R-meta] fixed-effect multivariate model interpretation
> >
> >Thank you Wolfgang!
> >So my related question is how this residual heterogeneity is estimated in
> order
> >to compute the Q statistic? Because if the model is still estimating and
> testing
> >the presence of heterogeneity, from a multivariate model I would have
> expected
> >one residual heterogeneity term for each outcome (the same as I have one
> tau per
> >outcome if I fit the random-effect version).
> >
> >On Mon, 3 Jan 2022 at 16:50, Viechtbauer, Wolfgang (SP)
> ><wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
> >Hi Filippo,
> >
> >You can *assume* that there is no residual heterogeneity, but there may
> be. That
> >is what the test of residual heterogeneity is testing here (whether your
> >assumption is correct or not).
> >
> >Best,
> >Wolfgang
> >
> >>-----Original Message-----
> >>From: R-sig-meta-analysis [mailto:
> r-sig-meta-analysis-bounces using r-project.org] On
> >>Behalf Of Filippo Gambarota
> >>Sent: Monday, 03 January, 2022 16:42
> >>To: R meta
> >>Subject: [R-meta] fixed-effect multivariate model interpretation
> >>
> >>Hello!
> >>I'm fitting for the first time a multivariate fixed-effect model using
> >>metafor. The code is:
> >>
> >>```
> >>rma.mv(yi, V, mods = ~ 0 + outcome, data = data, test = "t")
> >>```
> >>Where V is the block variance-covariance matrix created with vcalc()
> >>that represents the covariance between different outcome levels within
> >>each study. The outcome is a factor that represents different effect
> >>sizes measured on the same participants within a study.
> >>The model as expected did not estimate tau for each outcome and test
> >>all coefficients (each outcome mean with this parametrization) against
> >>0 (both the omnibus test and each beta). My question is about the
> >>*residual heterogeneity* parameter and the associated Q test. Under
> >>this model, I should have assumed that there is no heterogeneity
> >>within each outcome level so I'm not sure how to interpret the
> >>residual heterogeneity in this case.
> >>Thank you!
> >>Filippo
> >>
> >>--
> >>Filippo Gambarota
> >>PhD Student - University of Padova
> >>Department of Developmental and Social Psychology
> >>Website: filippogambarota.netlify.app
> >>Research Group: Colab   Psicostat

*Filippo Gambarota*
PhD Student - University of Padova
Department of Developmental and Social Psychology
Website: filippogambarota.netlify.app
Research Group: Colab <http://colab.psy.unipd.it/>   Psicostat

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