[R-meta] Question about longitudinal that report multiple outcomes

-----Original Message-----
From: Jack Solomon [mailto:kj.jsolomon at gmail.com]
Sent: Friday, 30 April, 2021 1:44
To: Viechtbauer, Wolfgang (SP)
Cc: r-sig-meta-analysis at r-project.org
Subject: Re: Question about longitudinal that report multiple outcomes

Thank you Wolfgang. I realized I confused something in my previous email. I meant
to say the following:

1- If I wanted to fit the `non-multivariate` parametrization of "random = list(~
outcome | study, ~ time| interaction(study,outcome))", then, would that have been:

"random = ~ 1| study/outcome/time"? 

If yes, could I then use "time" as a fixed
predictor as well? (If no, then, I think the multivariate parametrization is the
only specification that allows "time" to be used both as random and fixed (=
moderator), correct?)

2- Imagine we only have "time", then, when would it be appropriate to use: "random
= ~ 1| study/time/esID" [where "esID" is the row numbers]? and then could I use
"time" as a fixed moderator?

Conclusion: If I intend to use "time" or "outcome" both as random and fixed (=
moderator), we only should use the multivariate parametrization of the random
effect in the "rma.mv()" syntax, correct?

Thank you,
Jack

On Thu, Apr 29, 2021 at 3:08 AM Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
Please see below.

Best,
Wolfgang

-----Original Message-----
From: Jack Solomon [mailto:kj.jsolomon at gmail.com]
Sent: Thursday, 29 April, 2021 4:08
To: Viechtbauer, Wolfgang (SP)
Cc: r-sig-meta-analysis at r-project.org
Subject: Re: Question about longitudinal that report multiple outcomes

Dear?Wolfgang,

Thank you so much for your response.

Wolfgang: I can't quite parse your question. We are saying that the two 1.1
effects are dependent and that the two 1.2 effects are dependent and that the two
2.1 effects are dependent. But the two 1.1 effects are not correlated with any of
the other effects and so on. If this is what you meant, then yes.

++++ My question is that shouldn't 1.1, and 1.1 be correlated with 1.2 and 1.2 as
they belong to "study 1"? Is n't the goal to let the effect sizes from each study
to be correlated with each other due to multiple outcomes and multiple?time
points,?and let the effect sizes from different studies to be uncorrelated with
each other due to being from independent?studies?

Yes, but that's not the purpose of the '~ time | interaction(study,outcome)' term
which you asked about. This term allows for correlation among multiple effect
sizes obtained over time from the same study and the same outcome. The correlation
of different outcomes from the same study occurs via the '~ outcome | study' term.

++++ I imagine "~time | interaction(study,outcome)" is the same as "~time |
study/outcome", correct?

If this would work in rma.mv() (which does not), then '~ time | study/outcome'
should expand into the two terms '~ time | study' and '~ time |
interaction(study,outcome)' (if we would assume the same behavior as in lme()), so
this is not the same as only adding '~ time | interaction(study,outcome)' or
list(~ outcome | study, ~ time | interaction(study,outcome).

This aside, rma.mv() does not allow terms of the form '~ inner | outer1/outer2'.
If you want to do this, then you have to do list(~ inner | outer1, ~ inner |
interaction(outer1, outer2').

If yes, why not? jus "random = ~time |
study/outcome/esID", and instead creating?"random = list(~ outcome | study, ~

time

| study/outcome)"?

Since the answer was no, I'll skip this.

Thank you for your knowledge,
Jack