[R-meta] Difference between univariate and multivariate parameterization

Fri, Aug 20, 2021 4:31 AM

Dear Luke,

tau^2 doesn't mean the same thing across different models. In res5, the two tau^2 values can be thought of as sigma^2_within for single vs multi sample studies. Whether we call something tau^2, sigma^2, or chicken^2 doesn't carry any inherent meaning.

For example:

dat <- dat.crede2010
dat <- escalc(measure="ZCOR", ri=ri, ni=ni, data=dat, subset=criterion=="grade")

dat$studyid.copy <- dat$studyid
dat$sampleid.copy <- paste0(dat$studyid, ".", dat$sampleid)
rma.mv(yi, vi, random = ~ 1 | studyid/sampleid, data=dat)
rma.mv(yi, vi, random = list(~ studyid | studyid.copy, ~ sampleid | sampleid.copy), struct=c("ID","ID"), data=dat)

are identical models, but in the first we have two sigma^2 values and in the other we have tau^2 and gamma^2 (a bit of a silly example, but just to illustrate the point).

Best,
Wolfgang

-----Original Message-----
From: Luke Martinez [mailto:martinezlukerm at gmail.com]
Sent: Thursday, 19 August, 2021 5:05
To: Viechtbauer, Wolfgang (SP)
Cc: Farzad Keyhan; R meta
Subject: Re: [R-meta] Difference between univariate and multivariate
parameterization

Dear?Wolfgang,

Thanks for your reply. But, if in the multivariate specification: tau^2 =
sigma^2_between? +? sigma^2_within, then in your suggested "res5" model where you
fixed tau2 = 0 for single sample studies, you have killed both sigma^2_between +
sigma^2_within, and not just sigma^2_within?

Am I missing something?

Thank you very much,
Luke

On Wed, Aug 18, 2021 at 3:01 PM Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
It is also possible to formulate a model where sigma^2_within is *not* added for
'single sample/estimate studies'. Let's consider this example:

library(metafor)

dat <- dat.crede2010
dat <- escalc(measure="ZCOR", ri=ri, ni=ni, data=dat, subset=criterion=="grade")

table(dat$studyid) # most studies are single sample studies

# multilevel model
res1 <- rma.mv(yi, vi, random = ~ 1 | studyid/sampleid, data=dat)
res1

# multivariate parameterization
res2 <- rma.mv(yi, vi, random = ~ factor(sampleid) | studyid, data=dat)
res2

# as a reminder, the multilevel model is identical to this formulation
dat$sampleinstudy <- paste0(dat$studyid, ".", dat$sampleid)
res3 <- rma.mv(yi, vi, random = list(~ 1 | studyid, ~ 1 | sampleinstudy),
data=dat)
res3

# logical to indicate for each study whether it is a multi sample study
dat$multsample <- ave(dat$studyid, dat$studyid, FUN=length) > 1

# fit model that allows for a different sigma^2_within for single vs multi sample
studies
res4 <- rma.mv(yi, vi, random = list(~ 1 | studyid, ~ multsample | sampleinstudy),
struct="DIAG", data=dat)
res4

# fit model that forces sigma^2_within = 0 for single sample studies
res5 <- rma.mv(yi, vi, random = list(~ 1 | studyid, ~ multsample | sampleinstudy),
struct="DIAG", tau2=c(0,NA), data=dat)
res5

So this is all possible if you like.

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On
Behalf Of Farzad Keyhan
Sent: Wednesday, 18 August, 2021 21:32
To: Luke Martinez
Cc: R meta
Subject: Re: [R-meta] Difference between univariate and multivariate
parameterization

Dear Luke,

In the multivariate specification (model 2), tau^2 = sigma^2_between? +
sigma^2_within. You can confirm that by your two models' output as well.
Also, because rho = sigma^2_between / (sigma^2_between? +? sigma^2_within),
then, the off-diagonal elements of the matrix can be shown to be rho*tau^2
which again is equivalent to sigma^2_between in model 1's matrix.

Note that sampling errors in a two-estimate study could be different hence
appropriate subscripts will be needed to distinguish between them.

Finally, note that even a study with a single effect size estimate gets the
sigma^2_within, either directly (model 1) or indirectly (model 2) which
would mean that, that one-estimate study **could** have had more estimates
but it just so happens that it doesn't as a result of some form of
multi-stage sampling; first studies, and then effect sizes from within
those studies.

I actually raised this last point a while back on the list (
https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2021-July/002994.html)
as I found this framework a potentially unrealistic but in the end, it's
the best approach we have.

Cheers,
Fred

On Wed, Aug 18, 2021 at 1:30 PM Luke Martinez <martinezlukerm at gmail.com>
wrote:

Dear Colleagues,

Imagine I have two models.

Model 1:

random = ~1 | study / row_id

Model 2:

random = ~ row_id | study,? struct = "CS"

I understand that the diagonal elements of the variance-covariance matrix
of a study with two effect size estimates for each model will be:

Model 1:

VAR(y_ij) = sigma^2_between? +? sigma^2_within + e_ij

Model 2:

VAR(y_ij) = tau^2 + e_ij

Question: In model 2's variance-covariance matrix, what fills the role of
sigma^2_within (within-study heterogeneity) that exists in model 1's
matrix?

Thank you very much for your assistance,
Luke Martinez

[R-meta] Difference between univariate and multivariate parameterization

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