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

[Stefanou Revesz]

Thank you so much, Wolfgang and Michael. So, based on your advice I
will use the following:

rma.mv(es ~ time_meaning_wks, random = ~ time_meaning_wks | study,
struct = "CAR")

Then I think, the fixed coef. of "time_meaning_wks" is interpreted as:
The change in true effects for 1 week change in time regardless of the
 testing occasions' indicator.

One other option that we came up with to keep time_id (as a factor) in
the model was to control for "time_meaning_wks" as in:

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

Does this alternative make sense?

Thank you for your expertise,
Stefanou

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