[R-meta] How does the rma.mv function handle multiple inferences within a study-level
Viechtbauer, Wolfgang (SP)
wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Wed Apr 1 22:21:11 CEST 2020
Dear Divya,
The model you are using implies the following structure for the marginal var-cov matrix of the estimates:
[SE_1^2 + sigma^2 ]
[ SE_2^2 + sigma^2 ]
[ SE_3^2 + sigma^2 sigma^2 ]
[ sigma^2 SE_4^2 + sigma^2]
The weight matrix is the inverse thereof. See:
library(metafor)
case <- data.frame(Study=c("a","b","c","c"), ES=c(-1.5,-3,1.5,3), SE=c(.2,.4,.2,.4))
res <- rma.mv(ES, SE^2, random = ~ 1 | Study, data=case)
res
vcov(res, type="obs")
weights(res, type="matrix")
The model estimate is then given by b = (X'WX)^(-1) X'Wy, where X is just a column vector of 1s, W is the weight matrix above, and y is a column vector with the 4 effect sizes.
Best,
Wolfgang
-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On Behalf Of Divya Ravichandar
Sent: Wednesday, 01 April, 2020 21:59
To: r-sig-meta-analysis using r-project.org
Subject: [R-meta] How does the rma.mv function handle multiple inferences within a study-level
My use case is presented in the dataframe below. Studies a,b and c are to
be integrated in a meta-analysis using: rma.mv(ES, SE^2, random = ~ 1 |
Study, data=case)
In this case, studies a & b have one inference each but because of my study
design two inferences exist for study c. I am curious as to how the 2
inferences under study c are weighted in the meta-analysis calculation as
compared to the inference for studies a &b.
case <- data.frame(Study=
c("a","b","c","c"),Effect_size=c(-1.5,-3,1.5,3),Standard_error=c(.2,.4,.2,.4))
Thanks
--
*Divya Ravichandar*
Scientist
Second Genome
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