[R-meta] Appending a risk-of-bias traffic-light plot to a 'three-level' forest plot
Viechtbauer, Wolfgang (SP)
wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Fri Feb 18 17:58:39 CET 2022
I would suggest to use test="t" with dfs="contain". There is a bit of discussion here:
https://wviechtb.github.io/metafor/reference/rma.mv.html#tests-and-confidence-intervals
Roughly, z-tests tend to be too liberal, which we can counteract by using t-tests. But the default df calculation is quite simplistic and the "contain" method is an improvement on that. Still far from perfect, but a bit better.
Best,
Wolfgang
>-----Original Message-----
>From: Joshua Bernal [mailto:jdkb9701 using connect.hku.hk]
>Sent: Friday, 18 February, 2022 17:23
>To: Viechtbauer, Wolfgang (SP); r-sig-meta-analysis using r-project.org
>Subject: Re: [R-meta] Appending a risk-of-bias traffic-light plot to a 'three-
>level' forest plot
>
>Dear Dr. Viechtbauer,
>
>Thanks very much for your timely help! I'm delighted to learn about a
>simple alternative approach for creating the traffic light part. I
>would like to follow-up on the first part of the query on rma.mv /
>aggregated data...
>
>As the help page of the rma.mv function mentions: one can set
>dfs="contain" (which automatically also sets test="t")
>
>I checked to see whether the results would be the same as test="t" if
>I specify dfs="contain", and whether specifying test="t" with versus
>without dfs="contain" would yield the same results.
>
># dfs="contain"
>res <- rma.mv(yi, vi, random = ~ 1 | author/outcome, data = dat, dfs =
>"contain", method = "REML", slab = author)
>
>estimate se zval pval ci.lb ci.ub
> 0.2495 0.0738 3.3828 0.0007 0.1049 0.3940 ***
>
># test="t"
>res <- rma.mv(yi, vi, random = ~ 1 | author/outcome, data = dat, test
>= "t", method = "REML", slab = author)
>
>estimate se tval df pval ci.lb ci.ub
> 0.2495 0.0738 3.3828 15 0.0041 0.0923 0.4067 **
>
># test="t" and dfs="contain"
>res <- rma.mv(yi, vi, random = ~ 1 | author/outcome, data = dat, test
>= "t", dfs = "contain", method = "REML", slab = author)
>
>estimate se tval df pval ci.lb ci.ub
> 0.2495 0.0738 3.3828 5 0.0196 0.0599 0.4391 *
>
>I was wondering why the results differ, and importantly when would it
>be appropriate or sensible to use each of these approaches?
>Ultimately, I would like to know how I should determine the
>appropriate method for this part of the meta-analysis and generally
>what to consider when doing so (e.g., study sample size, number of
>studies, number of effect estimates per study)? For example, is it
>acceptable to use test="z" for a meta-analysis of eight studies with
>sample sizes of 66, 50, 38, 23, 23, 18, 12, 10 versus five studies
>with sample sizes of 50, 35, 28, 12, 10; or is it more sensible to be
>'conservative' and use test="t" in either one (or both) cases?
>
>Best regards,
>Josh
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