-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On
Behalf Of Reza Norouzian
Sent: Saturday, 28 August, 2021 22:51
To: Jorge Teixeira
Cc: R meta
Subject: Re: [R-meta] MLMA - shared control group
Please see below.
1) If I get things right, can we copy+paste the matrix code and it
always works in similar cases?
If ALL studies are structured like what Wolfgang demonstrated based on
Gleser & Olkin's chapter, yes. But note that this formula assumes
that, for example, all studies have measured their subjects on a
single outcome. If you have some studies that in addition to having
several treatment groups have more than one outcome, or have used one
or more post-tests then, this may not be useful in those cases
(although extensions are possible for those cases).
One way to avoid all the headache is to guesstimate the correlation
among effects due to perhaps several sources of sampling dependence
using V <- clubSandwich::impute_covariance_matrix(), and then, feed it
into the rma.mv() function via its V argument.
There are plenty of examples of this if you search the archives.
2) For meta-regression, we also have to use V, not vi, correct?
In the end, you need to input either vi or V. If you use vi, then
you're ignoring sampling dependence. If you ignore such sampling
dependence, no major harm is done to your estimates of average effects
(fixed effects), but your estimate(s) of how variable your effects at
each level may be systematically biased (i.e., even if you have a very
large dataset, you may not still obtain the true value of
heterogeneity).
If you don't care about heterogeneity of effect sizes, then knowing
about "any correlation among effect sizes" is not necessary, and you
can only use vi.