[R-meta] rma.mv: why some var components change but others don't across 2 models

Yes, if the values of 'outcome' have inherent meaning, you can consider
using it as a crossed random effect. That does not actually exclude the
possibility of adding another random effect nested within studies, that is:

random = list(~ 1 | study / id, ~ 1 | outcome, ~ 1 | measure)

where 'id' is unique to every row in the dataset.

Best,
Wolfgang

-----Original Message-----
From: Stefanou Revesz [mailto:stefanourevesz at gmail.com]
Sent: Tuesday, 02 November, 2021 13:58
To: Viechtbauer, Wolfgang (SP)
Cc: R meta
Subject: Re: rma.mv: why some var components change but others don't

across 2

models

Thanks. In my case, each 'outcome' means the same thing across the

studies. I

take 'measure' as a crossed random effect, because I believe each

'measure' has

its own inherent characteristics (its own questioning style, its own

length etc)

that affect effect sizes similarly in any study it has been used.

Thus, by taking 'measure' as a crossed random-effect, I account for the
dependence in effect sizes attributed to the use of a common 'measure'

*anywhere*

in the data.

But I can say the same thing for 'outcome'. If each 'outcome' has an

inherent

nature (math vs. history), then one can make the same argument that

applied to

'measure', and use 'outcome' as a crossed random effect, no?

(Or maybe, accounting for the within study heterogeneity due to the use of
different outcomes should still be preferred.)

On Tue, Nov 2, 2021, 1:41 AM Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
Unless the values of 'outcome' are meaningful and not just (essentially
arbitrary) values to distinguish different rows, using something like '~

1 |

outcome' makes no sense. For example, say the coding looks like this:

study outcome yi vi
1     1       .  .
1     2       .  .
2     1       .  .
2     2       .  .
2     3       .  .
3     1       .  .
...

'~ 1 | study / outcome' makes sense to allow for between- and within-study
heterogeneity. But unless a "1" for outcome in study 1 represents the

same type

of outcome as "1" is study 2 and 3, 'list(~ 1 | study, ~ 1 | outcome')

makes no

sense. If the numbers or values are only used to distinguish different

outcomes

within the same study but carry no inherent meaning beyond that, then one

could

just as well have coded the studies as:

study outcome yi vi
1     1       .  .
1     2       .  .
2     3       .  .
2     4       .  .
2     5       .  .
3     6       .  .
...

and '~ 1 | study / outcome' would give identical results to the previous

coding,

but 'list(~ 1 | study, ~ 1 | outcome') would not. In fact, with the second
coding, '~ 1 | study / outcome' and 'list(~ 1 | study, ~ 1 | outcome') are
identical (because the second coding is implicitly creating the same

nesting that

'~ 1 | study / outcome' implies).

Regardless of the coding, '~ 1 | study / outcome' and '~ outcome | study'

with

struct="CS" is identical (strictly speaking, the latter allows for a

negative

correlation and if so, then the equivalence breaks down, but let's not

get into

this). Structures like "HCS" and "UN" only make sense again when the

values of

'outcome' are inherently meaningful and not just arbitrary identifiers.

Best,
Wolfgang

-----Original Message-----
From: Stefanou Revesz [mailto:stefanourevesz at gmail.com]
Sent: Monday, 01 November, 2021 17:20
To: Viechtbauer, Wolfgang (SP)
Cc: R meta
Subject: Re: rma.mv: why some var components change but others don't

across 2

models

Thanks! Feel free to ignore this, but I don't think it has come up on
the mailing list before.

If I use: list(~ 1 | study, ~1|outcome, ~ 1 | measure), then
everything else aside, it means I believe that there are inherent
differences in 'outcome' that would necessitate disentangling
'outcome' effects from those of study and measure (crossing outcome
with study and measure).

On the other hand, I can use list(~ outcome | study, ~ 1 | measure),
struct="UN" which again adheres to the belief that there are inherent
differences in 'outcome' without necessitating disentangling 'outcome'
effects from those of study and measure (outcome nested in study).

What's the difference between the two strategies above, and why I
never see: list(~ 1 | study, ~1|outcome) in the archives (all I see is
either '~1|study/outcome' or its multivariate reparametrization '~
outcome | study'?

Stefanou