[R-meta] Multivariate meta-analysis when "some studies" are multi-outcome
Dear Wolfgang, Many thanks for your response. The reason I asked which level of dependence does V matrix account for was that I realized (at least when using 'impute_covariance_matrix()' function) that always the highest cluster level (e.g., study_id rather than outcome_id or es_id) is used to construct the V matrix. So, is there a reason for that? Many thanks On Thu, Mar 18, 2021, 6:38 AM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
Dear Simon, Roughly, whatever you put into 'random' accounts for heterogeneity in the true effects (at possibly multiple levels) and can account for possible dependencies in these true effects. Whatever you put into V accounts for the sampling variances in the estimates or more precisely, their sampling errors, and can account for possible dependencies in these sampling errors. I use the term 'dependencies' in a very vague/broad sense here, since such dependencies (in the true effects and/or the sampling errors) can arise for all kinds of different reasons. Best, Wolfgang
-----Original Message----- From: Simon Harmel [mailto:sim.harmel at gmail.com] Sent: Wednesday, 17 March, 2021 18:01 To: Viechtbauer, Wolfgang (SP) Cc: Gladys Barragan-Jason; R meta Subject: Re: [R-meta] Multivariate meta-analysis when "some studies" are
multi-
outcome Dear Wolfgang, I do want to quickly follow-up on the answer you linked (https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2018-July/000896.html
).
In `rma.mv(y ~ x1 + x2, V, random = ~ 1 | study/outcome/id, data=data)`,
we
apparently take into account dependence among effect sizes due to multiple treatments (`id`), and multiple outcomes (`outcome`) by means of using a
level for
each. If so, what is the role of `V` when it comes to accounting for effect size dependency? Does `V` simply determine the pair-wise structure of
effect size
dependency? If yes, at what level? Simon