Message-ID: <4e6488c3b0bc4c83bb462ca19b425e84@UM-MAIL3214.unimaas.nl>
Date: 2019-09-27T15:27:02Z
From: Wolfgang Viechtbauer
Subject: [R-meta] Covariance-variance matrix when studies share multiple treatment x control comparison
In-Reply-To: <SN6PR02MB4336AB2BC7B20A3B4DC78C1EF7810@SN6PR02MB4336.namprd02.prod.outlook.com>
You are still using 'residuals' as the outcome. Don't do that. Just use the Hedges' g values as the outcome. Also, you should specify the correct V matrix (I think you called it VCV in an earlier post). So, for example:
rma.mv(hedged ~ precision.2, VCV, data=MHF, random = list(~ 1 | Study, ~1 | Id), subset=(Spatial.scale.2=="Fragmentation scale"))
I haven't looked at your other post in detail, but 'random = ~ factor(x) | Study/Id' doesn't actually work (at least not in the way you think it does). Please update your metafor installation to get an error that will inform you of this. Instead, random = list(~factor(x)|Study, ~factor(x)|Id) is indeed the correct way to specify two '~ inner | outer' terms.
Best,
Wolfgang
-----Original Message-----
From: Ju Lee [mailto:juhyung2 at stanford.edu]
Sent: Friday, 27 September, 2019 16:46
To: James Pustejovsky
Cc: Viechtbauer, Wolfgang (SP); r-sig-meta-analysis at r-project.org
Subject: Re: Covariance-variance matrix when studies share multiple treatment x control comparison
Dear James,?Wolfgang,
Thank you very much for this information.
I have one question extending from this is: While I run my main mixed modes always using var-covar. matrix (to account for shared study groups within each study),?
it is acceptable that my egger-like regression does not incorporate this structure, but rather just use sqrt(1 / n1 + 1 / n2)? as precision (instead of sqrt(diag(v.c.matrix)) like?Wolfgang suggested as one possibility) and use p-value for precision term (precision.2 which is p=0.2382) to determine the asymmetry?
prec.<-function(CN,TN){
? pr<-sqrt((1 / CN) + (1/TN))
? return(pr)
}
precision.2<-prec.(MHF$n.t, MHF$n.c)
> egger.pr2.frag<-rma.mv(residuals~precision.2,var,data=MHF,random =list( ~ 1 | Study, ~1|Id), ?subset=(Spatial.scale.2=="Fragmentation scale"))
> egger.pr2.frag
Multivariate Meta-Analysis Model (k = 285; method: REML)
Variance Components:?
? estim ? ?sqrt ?nlvls ?fixed ?factor
sigma^2.1 ?0.4255 ?0.6523 ? ? 73 ? ? no ? Study
sigma^2.2 ?0.3130 ?0.5595 ? ?285 ? ? no ? ? ?Id
Test for Residual Heterogeneity:
? QE(df = 283) = 1041.1007, p-val < .0001
Test of Moderators (coefficient(s) 2):
? QM(df = 1) = 1.3909, p-val = 0.2382
Model Results:
? estimate ? ? ?se ? ? zval ? ?pval ? ?ci.lb ? ci.ub ?
intrcpt ? ? ? ?0.0529 ?0.1617 ? 0.3274 ?0.7433 ?-0.2640 ?0.3699 ?
precision.2 ? -0.0668 ?0.0567 ?-1.1794 ?0.2382 ?-0.1779 ?0.0442? ?
---
? Signif. codes: ?0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Thank you very much, both of you.
Best,
JU
p.s.?Wolfgang, I think I figured out what went wrong with how I specified my random effects in my previous e-mail. Specifying it as?random=list(~factor(x)|Study, ~factor(x)|Id)?instead of random= ~factor(x)|Study/Id generates results that makes sense to me now. Please let me know if this is correct way I should be coding.