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