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

Fabian Schellhaas f@bi@n@@chellh@@@ @ending from y@le@edu
Fri Oct 5 20:52:00 CEST 2018

Hi Wolfgang,

Thank you for your very helpful reply. I will gladly stick to the "best
guess" approach for the var-cov structure, and I will use LR tests to probe
whether it's justified leaving out the higher-order random effects.

Have a great weekend,

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

On Fri, Oct 5, 2018 at 11:37 AM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using 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 using r-project.org] On Behalf Of Fabian Schellhaas
> Sent: Thursday, 27 September, 2018 23:55
> To: r-sig-meta-analysis using 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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