[R-meta] How does the rma.mv function handle multiple inferences within a study-level
Thank you for your explanation of how the weight matrix is computed. A followup question, on the 'sigma^2' only terms in the variance matrix [terms in matrix positions (3,4) & (4.3)]. I assume (based on running the example above) the sigma^2 here is different from the sigma^2 used along the diagonal. Is this correct? If yes, is a sigma^2 estimated based on just the values corresponding to study c in the example? Thank you On Wed, Apr 1, 2020 at 1:21 PM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
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 at r-project.org] On Behalf Of Divya Ravichandar
Sent: Wednesday, 01 April, 2020 21:59
To: r-sig-meta-analysis at 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
*Divya Ravichandar* Scientist Second Genome [[alternative HTML version deleted]]