[R-meta] fixed-effect multivariate model interpretation
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 at 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 at 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 at 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 at 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 <https://psicostat.dpss.psy.unipd.it/> [[alternative HTML version deleted]]