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

[Michael Dewey]

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
> 

-- 
Michael
http://www.dewey.myzen.co.uk/home.html