[R-meta] Time as indicator vs time as meaning

Sun, Oct 10, 2021 6:56 AM

See below for my responses.

Best,
Wolfgang

Correct. And now comes the part again where I am being pedantic:

1) To be precise: The average change.

2) You could run this model even if not a single study had used two (or more) measurement occasions (one could then not estimate rho - so in principle, the model is then overparameterized, but this isn't relevant to the point I am trying to make here). In this case, the coefficient for time_meaning_wks is estimated purely by examining the size of the effect across studies that used different measurement weeks. In this case, speaking of 'change' might suggest that the studies are actually providing evidence about the change in the effect over time, when in fact the evidence is purely cross-sectional (or "cross-studinal" if there even is such a word - Google gives me 0 hits for this, so maybe not, but then I just invented it). You *do* have studies that used multiple measurement occasions, so the coefficient for time_meaning_wks is actually a mixture of cross-studinal evidence and actual changes in the effect that occurred within studies. To disentangle such between- and within-study evidence, one can apply methods that are well-known in the multilevel / longitudinal data analysis context, namely by computing the study-level means of time_meaning_wks and the deviations from these means within studies and then including both of these variables in the model.

One can do this, but it's trying to squeeze a lot of information out of your data. The factor(time_id) fixed effect is asking about differences in measurement occasions irrespective of their actual time values (so the second measurement occasions might be at 6 weeks in study 1 and at 24 weeks in study 2 but these are both treated identically). The time_meaning_wks effect is asking about (linear) changes (or differences -- see above) in the effect over time measured in weeks.

And as discussed in a previous post, using something like 'random = ~ factor(time_id) | study, struct = "UN"' is likely to lead to a rather large number of variance components and correlations being estimated.

Thank you for your expertise,
Stefanou

On Sat, Oct 9, 2021 at 11:13 AM Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:

To add to this:

2. Terms used in 'random' are not allowed to have missing values in rma.mv(),

so those rows will need to be filtered out first before fitting the model.

3. rho in "CAR" is the autocorrelation for a one-unit difference in the time

variable. So if time is measured in weeks, then rho reflects the correlation
between two time points one week apart.

Best,
Wolfgang

-----Original Message-----
From: Michael Dewey [mailto:lists at dewey.myzen.co.uk]
Sent: Saturday, 09 October, 2021 17:36
To: Stefanou Revesz; Viechtbauer, Wolfgang (SP)
Cc: R meta
Subject: Re: [R-meta] Time as indicator vs time as meaning

Comments in-line

On 09/10/2021 15:56, Stefanou Revesz wrote:

Dear Wolfgang,

Thank you for your reply. The rma.mv() documentation for CAR says:
"the values of the "inner" variable should reflect the exact time
points of the measurement".

1) Does that mean I should use:  "time_meaning_wks | study" OR
"time_id | study"?

Use the continuous one time_meaning_wks

2) Can I have missing in "time_meaning_wks"?

I assume it will work, just try it, nothing will break.

3) Do you possibly have a demonstration showing how to interpret CAR
(or any other useful references to read about CAR)?

If you type auto-regressive models into your favourite search engine you
should find plenty of material. There are a couple of examples of AR
models in the documentation, see ?rma.mv but neither of them is for a
continuous covariate.

Thank you very much,
Stefanou

On Sat, Oct 9, 2021 at 7:52 AM Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:

Indeed. But then struct="CAR" would probably be more

appropriate/parsimonious,

since "UN" will estimate a different tau^2 for every unique week value and a
different correlation for every possible pair of week values.

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-

project.org]

On

Behalf Of Michael Dewey
Sent: Saturday, 09 October, 2021 12:59
To: Stefanou Revesz; R meta
Subject: Re: [R-meta] Time as indicator vs time as meaning

Dear Stefanou

I think it would be find to use the continuous version both as fixed and
random effect.

Michael

On 09/10/2021 05:49, Stefanou Revesz wrote:

Dear Meta-Analysis Colleagues,

We are meta-analyzing 73 longitudinal studies. But we have doubts
amongst us regarding how to combine the longitudinal effects of these
studies.

On the one hand, if we use time only as an indicator of testing
occasions (pre-test and post-tests), and then use it as fixed and
random-effect as in:

rma.mv(es ~ time_id, random = ~ time_id | study, struct = "UN")

then, we have longitudinally combined apples and oranges. That is,
time 1 in one study may have covered six months, but time 1 in another
study may have covered 6 days. This, we think, is problematic in terms
of the interpretation of both the fixed and random-effects of time.

So, we have coded for both time_id (testing occasions indicator) and
time_meaning_wks (length of actual time up to each testing occasion in
weeks).

We are wondering how we should incorporate time_meaning_wks into our

model?

Any help is appreciated,
Stefanou

study  time_id   time_meaning_wks
1        0             0
1        1             4
1        2             6
2        0             0
2        1             1

[R-meta] Time as indicator vs time as meaning

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