[R-meta] Covariance-variance matrix when studies share multiple treatment x control comparison
Viechtbauer, Wolfgang (SP)
wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Fri Sep 27 17:27:02 CEST 2019
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.
From: Ju Lee [mailto:juhyung2 using stanford.edu]
Sent: Friday, 27 September, 2019 16:46
To: James Pustejovsky
Cc: Viechtbauer, Wolfgang (SP); r-sig-meta-analysis using 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?
pr<-sqrt((1 / CN) + (1/TN))
> egger.pr2.frag<-rma.mv(residuals~precision.2,var,data=MHF,random =list( ~ 1 | Study, ~1|Id), subset=(Spatial.scale.2=="Fragmentation scale"))
Multivariate Meta-Analysis Model (k = 285; method: REML)
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
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.
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.
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