[R-meta] [External] RE: 4-Level analysis in metafor

[Viechtbauer, Wolfgang (SP)]

See: https://www.metafor-project.org/doku.php/tips:i2_multilevel_multivariate

Best,
Wolfgang

>-----Original Message-----
>From: Harris, Jordan L [mailto:jordan-l-harris using uiowa.edu]
>Sent: Friday, 04 March, 2022 19:06
>To: Viechtbauer, Wolfgang (SP); r-sig-meta-analysis using r-project.org
>Subject: Re: [External] RE: 4-Level analysis in metafor
>
>Hi Wolfgang,
>
>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.
>
>Thanks,
>Jordan
>________________________________________
>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
>
>Hi Jordan,
>
>Sure it can. We have done 5-level models (including another crossed random
>effect) with rma.mv():
>
>https://wviechtb.github.io/metadat/reference/dat.mccurdy2020.html
>
>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.
>
>Best,
>Wolfgang
>
>>-----Original Message-----
>>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
>>
>>Hi all,
>>
>>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
>>analyzed
>>
>>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)
>>
>>Variance Components:
>>
>>            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
>>feedback!
>>Thanks,
>>Jordan