[R-meta] [External] RE: 4-Level analysis in metafor
Harris, Jordan L
jord@n-|-h@rr|@ @end|ng |rom u|ow@@edu
Fri Mar 4 19:06:09 CET 2022
Thank you very much for the reply!
Do you suggest any specific method for calculating the I2 variance between levels? I found a github package "dmetar" that allows for this calculation for 3-level, but will not allow for calculations greater than 3.
From: Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer using maastrichtuniversity.nl>
Sent: Friday, March 4, 2022 10:56 AM
To: Harris, Jordan L <jordan-l-harris using uiowa.edu>; r-sig-meta-analysis using r-project.org <r-sig-meta-analysis using r-project.org>
Subject: [External] RE: 4-Level analysis in metafor
Sure it can. We have done 5-level models (including another crossed random effect) with rma.mv():
How well the variance components can be estimated depends of course on how much data you have. And it can certainly happen that one components ends up being estimated to be (close to) zero.
I wouldn't bother removing that one level - that happens implicitly/automatically when a variance component is estimated to be 0.
>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On
>Behalf Of Harris, Jordan L
>Sent: Friday, 04 March, 2022 17:30
>To: r-sig-meta-analysis using r-project.org
>Subject: [R-meta] 4-Level analysis in metafor
>Does rma.mv appropriately account for between- and within-cluster variance for 4
>level nested data?
>rma.mv(yi=ES, V=sampling_variance, slab=authors, data=Data, random = list(~ 1 |
>datasource_id/wave_id/study), tdist=TRUE, method="REML")
>study_id = included study
>datasource = the source of data (e.g., large cohort study or independent samples)
>wave_id = the wave of the datasource (i.e., age) from which the study was
>Multiple effect sizes can occur at a given wave in a given data source. Multiple
>effect sizes also exist in a given study at a given wave. Provided this
>information, it might be important to nest studies within waves within data
>sources. I ask because I see that the sigma^2.2. estimate of my output is nearly
>0 and I was not sure if this is an accurate reflection of my data or metafor's
>ability to account for differences at this added level? Should I use the 0
>estimate at 2.2 to justify a removal of wave_id from the nesting?
>Multivariate Meta-Analysis Model (k = 100; method: REML)
> estim sqrt nlvls fixed factor
>sigma^2.1 0.0069 0.0832 41 no datasource_id
>sigma^2.2 0.0000 0.0000 60 no datasource_id/wave_id
>sigma^2.3 0.0023 0.0482 82 no datasource_id/wave_id/study_id
>I am a graduate student, and I am new to meta-analyses, and I would love any
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