[R-meta] Var-cov structure in multilevel/multivariate meta-analysis

Aha I see. It?s not necessary to impute the correlation matrix at the same
level as you cluster the standard errors. I would suggest imputing the
correlation matrix at the sample level, because it is only at this level
that there is dependence in the sampling errors (conditional on true
effects for the sample). At higher levels, the sampling errors will be
independent but there may be dependence between true effects (hierarchical
dependence) due to use of common procedures, interventions, or whatnot.

But then for purposes of clustering, we need to ?catch? all forms of
dependence (both sampling dependence and hierarchical dependence) and so
would cluster at the highest level.

James

On Mar 23, 2019, at 11:39 AM, Fabian Schellhaas <
fabian.schellhaas at yale.edu> wrote:

Hi James,

I suppose the variance components change because we're now imputing the
variance-covariance matrix at a higher level of clustering. My
understanding has been that the vcov imputation needs to be done at the
same level of clustering used for the estimation of cluster-robust standard
errors later, but I'm keen to hear what your take is. I included more
details below.

Initially, the model was (incorrectly) fitted as follows:
vcv <- clubSandwich::impute_covariance_matrix(vi = data$vi, cluster =
data$sample_id, r = 0.7)
m <- metafor::rma.mv(yi, V = vcv, random = ~ 1 | paper_id/comp_id/es_id)

Variance components: sigma^2.1 = 0.0068, sigma^2.2 = 0.0087, sigma^2.3 =
0.0137
Comparing this model to one model with random effects only for comparisons
and estimates: LRT = 15.11, p < .001

Now, fitting the model with the vcov matrix imputed at the level of papers:
vcv <- clubSandwich::impute_covariance_matrix(vi = data$vi, cluster =
data$paper_id, r = 0.7)
m <- metafor::rma.mv(yi, V = vcv, random = ~ 1 | paper_id/comp_id/es_id)

Variance components: sigma^2.1 = 0.0012, sigma^2.2 = 0.0131, sigma^2.3 =
0.0142
Comparing this model to one model with random effects only for comparisons
and estimates: LRT = 0.59, p = .443

Many thanks,
Fabian

---
Fabian M. H. Schellhaas | Ph.D. Candidate | Department of Psychology |
Yale University


On Sat, Mar 23, 2019 at 12:18 PM James Pustejovsky <jepusto at gmail.com>
wrote:

Fabian,

I don?t follow the new results you?ve just described. I meant clustering
as in the clustering variable specified when computing robust standard
errors. Switching this to a higher level should not affect your variance
component estimates at all?only the SEs of the fixed effects will change.
So I?m not sure what you?ve modified that leads to changes in the variance
component estimates.

James

On Mar 23, 2019, at 11:10 AM, Fabian Schellhaas <
fabian.schellhaas at yale.edu> wrote:

Hi James,

Thanks a lot for the quick reply -- this makes sense. I just checked and
clustering at a higher level instead (e.g., study) actually reduces the
variance component for papers so much that retaining it does not improve
model fit, so I suppose that random effect would no longer be needed? More
generally, is it a correct rule of thumb to specify clustering at the
highest/outermost level, and then model hierarchical dependence at lower
levels by adding random effects as needed?

Thanks!
Fabian


On Sat, Mar 23, 2019 at 11:41 AM James Pustejovsky <jepusto at gmail.com>
wrote:

Fabian,

That will not work. Cluster-robust standard errors require that the
clusters are independent, and so clustering at the level of the sample will
fail to capture dependence at higher levels (studies and papers). I think
the appropriate approach here is to cluster at the highest level (papers).

James

On Mar 22, 2019, at 5:41 PM, Fabian Schellhaas <

fabian.schellhaas at yale.edu> wrote:

Hi Wolfgang and mailing list,

I would like to follow up on point (3) from this old thread. Quick
refresher of the data structure: We have (approximately) 650 effects

from

400 treatment-control comparisons, which come from 325 independent

samples,

nested in 275 studies from 200 papers. Many of the samples include more
than one treatment-control comparison and evaluate their effect on more
than one outcome measure, resulting in correlated residuals clustered

at

the level of independent samples.

First, we model the hierarchical dependence of the true effects. LRTs
indicate that model fit is improved significantly by adding random

effects

for treatment-control comparisons (relative to a single-level model)

and

further improved by adding random effects for papers (relative to the
two-level model). Adding random effects for samples and studies did not
improve on the two-level model. So in short, we include random effects

for

papers, treatment-control comparisons, and individual estimates,

skipping

studies and samples. Second, to deal with the nonindependent

residuals, due

to multiple comparisons and multiple outcome measures, we use
cluster-robust variance estimation. In our data, residuals are

clustered at

the level of independent samples.

As such, we could fit a model as follows:
vcv <- clubSandwich::impute_covariance_matrix(vi = data$vi, cluster =
data$sample_id, r = 0.7)
m <- metafor::rma.mv(yi, V = vcv, random = ~ 1 |

paper_id/comp_id/es_id)

clubSandwich::coef_test(m, cluster = data$sample_id, vcov = "CR2")

Are there any problems with computing robust standard errors at a

level of

clustering (here: samples) that does not correspond to the levels at

which

hierarchical dependence of the true effects are modeled (here: papers

and

treatment-control comparisons)? If so, what would be a better approach?

Many thanks!
Fabian


On Fri, Oct 5, 2018 at 11:37 AM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:

Hi Fabian,

(very clear description of the structure, thanks!)

1) Your approach sounds sensible.

2) If you are going to use cluster-robust inference methods in the end
anyway, then getting the var-cov matrix of the sampling errors

'exactly

right' is probably not crucial. It can be a huge pain constructing the
var-cov matrix, especially when dealing with complex data structures

as you

describe. So, sticking to the "best guess" approach is probably

defensible.

3) It is difficult to give general advice, but it is certainly

possible to

add random effects for samples, studies, and papers (plus random

effects

for the individual estimates) here. One can probably skip a level if

the

number of units at a particular level is not much higher than the

number of

units at the next level (the two variance components are then hard to
distinguish). So, for example, 200 studies in 180 papers is quite

similar,

so one could probably leave out the studies level and only add random
effects for papers (plus for samples and the individual estimates).

You can

also run likelihood ratio tests to compare models to see if adding

random

effects at the studies level actually improves the model fit

significantly.

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:
r-sig-meta-analysis-bounces at r-project.org] On Behalf Of Fabian

Schellhaas

Sent: Thursday, 27 September, 2018 23:55
To: r-sig-meta-analysis at r-project.org
Subject: [R-meta] Var-cov structure in multilevel/multivariate
meta-analysis

Dear all,

My meta-analytic database consists of 350+ effect size estimates,

drawn

from 240+ samples, which in turn were drawn from 200+ studies,

reported in

180+ papers. Papers report results from 1-3 studies each, studies

report

results from 1-2 samples each, and samples contribute 1-6 effect sizes
each. Multiple effects per sample are possible due to (a) multiple
comparisons, such that more than one treatment is compared to the same
control group, (b) multiple outcomes, such that more than one outcome

is

measured within the same sample, or (c) both. We coded for a number of
potential moderators, which vary between samples, within samples, or

both.

I included an example of the data below.

There are two main sources of non-independence: First, there is
hierarchical dependence of the true effects, insofar as effects

nested in

the same sample (and possibly those nested in the same study and

paper) are

correlated. Second, there is dependence arising from correlated

sampling

errors when effect-size estimates are drawn from the same set of
respondents. This is the case whenever a sample contributes more than

one

effect, i.e. when there are multiple treatments and/or multiple

outcomes.

To model these data, I start by constructing a ?best guess? of the

var-cov

matrices following James Pustejovsky's approach (e.g.,

https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2017-August/000094.html

),
treating samples in my database as independent clusters. Then, I use

these

var-cov matrices to construct the multilevel/multivariate

meta-analytic

model. To account for the misspecification of the var-cov structure, I
perform all coefficient and moderator tests using cluster-robust

variance

estimation. This general approach has also been recommended on this

mailing

list and allows me (I think) to use all available data, test all my
moderators, and estimate all parameters with an acceptable degree of
precision.

My questions:

1. Is this approach advisable, given the nature of my data? Any

problems I

missed?

2. Most manuscripts don?t report the correlations between multiple
outcomes, thus preventing the precise calculation of covariances for

this

type of dependent effect size. By contrast, it appears to be fairly
straightforward to calculate the covariances between

multiple-treatment

effects (i.e., those sharing a control group), as per Gleser and Olkin
(2009). Given my data, is there a practical way to construct the

var-cov

matrices using a combination of ?best guesses? (when correlations

cannot be

computed) and precise computations (when they can be computed via

Gleser

and Olkin)? I should note that I?d be happy to just stick with the

?best

guess? approach entirely, but as Wolfgang Viechtbauer pointed out (

https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2017-August/000131.html

),
only a better approximation of the var-cov structure can improve

precision

of the fixed-effects estimates. That's why I'm exploring this option.

3. How would I best determine for which hierarchical levels to specify
random effects? I certainly expect the true effects within the same

set of

respondents to be correlated, so would at least add a random effect

for

sample. Beyond that (i.e., study, paper, and so forth) I?m not so

sure.

Cheers,

Fabian

### Database example:

Paper 1 contributes two studies - one containing just one sample, the

other

containing two samples ? evaluating the effect of treatment vs.

control on

one outcome. Paper 2 contributes one study containing one sample,
evaluating the effect of two treatments (relative to the same

control) on

two separate outcomes each. The first moderator varies between

samples, the

second moderator varies both between and within samples.

paper     study sample    comp es yi        vi mod1 mod2

1         1 1         1 1 0.x       0.x A A

1         2 2         2 2 0.x       0.x B B

1         2 3         3 3 0.x       0.x A B

2         3 4         4 4 0.x       0.x B A

2         3 4         4 5 0.x       0.x B C

2         3 4         5 6 0.x       0.x B A

2         3 4         5 7 0.x       0.x B C

---
Fabian Schellhaas | Ph.D. Candidate | Department of Psychology | Yale
University

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