[R-meta] rma.mv only for better SEs
Thank you, Wolfgang. I asked this, because I noticed applying RVE to an rma.mv() model has no bearing on the estimates of fixed effects themselves, and just modifies their SEs. So, I wondered if the same rule, at least "in principle", should apply when we go from rma() to rma.mv(). But is there a principle regarding how random effects affect the fixed effects? For instance, in: 1- rma.mv(y ~ 1, random = ~ 1|study/obs), the overall average only represents the average of study-level effects. But, in: 2- rma.mv(y ~ 1, random = ~ 1|study/outcome/obs), the overall average represents the average of study-level effects additionally affected by the outcome-level effects within them. And thus, 1- and 2- may give different overall averages, right? Simon On Mon, Jan 31, 2022 at 11:00 AM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
Generally, two models with different random effects structures will also give you different estimates of the fixed effects (unless the estimates of the variance/covariance components happen to be such that the two models collapse down to the same structure). Best, Wolfgang
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Behalf Of Simon Harmel Sent: Monday, 31 January, 2022 17:49 To: R meta Subject: [R-meta] rma.mv only for better SEs Hello List Members, Reviewing the archived posts, my understanding is that my studies can produce multiple effects, so I should use rma.mv() not rma(). Also, I understand rma.mv() ensures that I get more accurate SEs for my fixed effects relative to rma(). BUT does that also mean that, by definition, rma.mv() should have no bearing on the magnitude of the fixed effects themselves and only modifies their SEs relative to rma()? Thank you, Simon