[R-meta] Question about Meta analysis

-----Original Message-----
From: Maximilian Steininger <maximilian.steininger at univie.ac.at>
Sent: Monday, May 6, 2024 17:09
To: Viechtbauer, Wolfgang (NP) <wolfgang.viechtbauer at maastrichtuniversity.nl>
Cc: R Special Interest Group for Meta-Analysis <r-sig-meta-analysis at r-
project.org>
Subject: Re: [R-meta] Question about Meta analysis

Dear Wolfgang,

as always many thanks!

In study 3, there is just a single row. Just to be clear: You are referring to

a 'test-retest r = 0.9' but this has no bearing on the sampling variance in V.
If it is a within-study design, the computation of its sampling variance already
should have been done in such a way that any pre-post correlation is accounted
for.

I see, that clears things up.

I am trying to understand your coding for study 4 ("Within-study with one

control and two intervention conditions"), which you coded as follows:

  studyid esid design subgroup type time1 time2 grp1 grp2  ne nc  yi   vi
5        4    5      2        1    1     1     2    e    c  40 40 0.5 0.05
6        4    6      2        1    1     1     3    e    c  40 40 0.6 0.05
But this coding implies that there are two independent groups, e and c, where

e was measured at time point 1 and c at time points 2 and 3. I am not sure if I
really understand this design.

I guess in that case I just mis-specified. If it is a pure within-design (always
same subjects in every condition) then the coding for both grp1 and grp2 is
supposed to always have the same value (so ?e" for each cell)? Seems like I got
that wrong, thanks for making me aware.

For study 6, your coding is:
  studyid esid design subgroup type time1 time2 grp1 grp2  ne nc  yi   vi
11       6   11      2        1    1     1     2    c    c  90 90 1.1 0.05
12       6   12      2        1    2     1     2    c    c  90 90 1.2 0.05

But I think the coding should be:
  studyid esid design subgroup type time1 time2 grp1 grp2  ne nc  yi   vi
11       6   11      2        1    1     1     2    e    e  90 90 1.1 0.05
12       6   12      2        1    2     1     2    e    e  90 90 1.2 0.05

Makes sense.

In study 5, there are two subgroups. Since there is (presumably) no overlap of

subjects across subgroups, the sampling errors across subgroups are independent,
so we just have two cases of what we have in study 2.

Agreed, and by specifying  "random = ~ 1 | studyid/esid?  in my model the
dependency of the effect sizes from that study should be taken care of.

I recently added measure "SMCRP" to escalc(). This uses the pooled SD from pre

and post to standardize the difference.
Great! Just out of curiosity is it based on the approach by Cousineau, 2020?
doi: 10.20982/tqmp.16.4.p418

Thanks a lot for taking the time and your help!

Best,
Max