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

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

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_interc
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 


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