[R-meta] Interpretation of continuous moderator in 3-level meta-regression

[d@@iei@i@gucci@rdi m@iii@g oii gm@ii@com]

Perfect - thanks for the quick response and confirmation Wolfgang.

Cheers,
Daniel

-----Original Message-----
From: Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer using maastrichtuniversity.nl> 
Sent: Thursday, 9 September 2021 12:01 AM
To: daniel.f.gucciardi using gmail.com; r-sig-meta-analysis using r-project.org
Subject: RE: [R-meta] Interpretation of continuous moderator in 3-level
meta-regression

Dear Daniel,

Yes, that's correct.

I would be cautious if there are no 1's in mod1 for the actual data, since
you would then be extrapolating, but I assume there are, so it's all good.

Best,
Wolfgang

>-----Original Message-----
>From: R-sig-meta-analysis 
>[mailto:r-sig-meta-analysis-bounces using r-project.org] On Behalf Of 
>daniel.f.gucciardi using gmail.com
>Sent: Wednesday, 08 September, 2021 8:54
>To: r-sig-meta-analysis using r-project.org
>Subject: [R-meta] Interpretation of continuous moderator in 3-level 
>meta- regression
>
>Hi all,
>
>I was hoping to seek your advice on the interpretation of a 3-level 
>meta-regression with a continuous variable. Briefly, my effect size 
>(yi) is sedentary time in minutes and moderator (mod1) is the 
>percentage of wear time for a 24-hour period for the device used to assess
sedentary behaviour.
>I have 55 effect sizes (esid) from 36 studies (studyid). The moderator 
>varies among effect sizes.
>
>I have coded the moderator on a scale of 0-1; for example, if someone 
>wore the device for 50% of the 24-hour period, we coded them as 0.50. I 
>removed the intercept in the moderator analysis because a value of 0 
>for the moderator should technically equate to 0 min for the effect 
>size 
>(https://www.metafor-project.org/doku.php/tips:models_with_or_without_i
>nterc
>ept).
>
>library(metafor)
>
>mods_result <- rma.mv(yi, vi,
>                      data = df,
>                      level = 95,
>                      method = "REML",
>                      tdist = TRUE,
>                      mods = ~mod1-1,
>                      random = ~1 | studyid/esid)
>
>summary(mods_result)
>
>Model Results:
>
>estimate       se     tval  df    pval     ci.lb     ci.ub
>mod1  802.2315  19.6706  40.7833  54  <.0001  762.7943  841.6686  ***
>
>I read this post with interest
>(https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2021-August/003125.
>html) , which used chronological age as the moderator. For my case, 
>should I interpret the estimate of approx. 802 min as the expect value 
>for the moderator at 1 (so 100% wear time)?
>
>Cheers,
>Daniel