[R-meta] Construct the covariance-matrices of different effect sizes

James Pustejovsky jepu@to @end|ng |rom gm@||@com
Tue Jan 19 15:53:30 CET 2021

Hi Tzlil,

Apologies for the long delay in responding to your query. You've raised
some excellent questions about rather subtle issues. To your question (1),
I would say that it is NOT compulsory to use a sampling variance-covariance
matrix (the "V-matrix") for the effect sizes of each type. Omitting the
V-matrix amounts to assuming a correlation of zero. If your goal is
primarily to understand average effect sizes (of each type), then using
robust standard errors/hypothesis tests/confidence intervals will work even
if you have a mis-specified assumption about the correlation among effect

That said, there are at least two potential benefits to using a V-matrix
based on more plausible assumptions about the correlation between effect
sizes. First, using a working model that is closer to the true dependence
structure will yield a more precise estimate of the average effect. If
you're just estimating an average effect of each type, the gain in
precision will probably be pretty small. If you're estimating a
meta-regression with a more complex set of predictors, the gains can be
more substantial.

The second potential benefit is that using a plausible V-matrix will give
you better, more defensible estimates of the variance components
(between-study variance and within-study variance). Whether based on REML
or some other estimation method, the variance component estimates are NOT
robust to mistaken assumptions about the sampling correlation structure.
They'll be biased unless you have the sampling correlation structure
approximately correct. So to the extent that understanding heterogeneity is
important, I think it's worth working on building in a V-matrix.

To your question (2), I like the approach you've outlined, where you use
different V-matrices for each of the effect indices you're looking at. I
think ideally, you would start by making a single assumption about the
degree of correlation between the *outcomes*, and then using that to derive
the appropriate degree of correlation between each of the indices:
* For raw mean differences, the correlation between outcomes will translate
directly into the correlation between mean differences.
* For SDs, I'm not sure exactly what your ES index is. Is it the log ratio
of SDs? How did you arrive at the formula for the correlation between
effect sizes? I don't know of a source for this, but it could be derived
via the delta method.
* For ICCs and pearson correlations, using Wolfgang's function would be the
way to go. Perhaps if you can provide a small example of your data and
syntax that you've attempted, folks on the list can provide guidance about
applying the function.

Kind Regards,

On Thu, Jan 7, 2021 at 5:16 PM Tzlil Shushan <tzlil21092 using gmail.com> wrote:

> Dear Wolfgang and James,
> Apologise for the long assay in advance..
> In my meta-analysis I obtained different effect sizes coming
> from test-retest and correlational designs. Accordingly, I performed 4
> different meta-analyses for each effect size:
> Raw mean difference of test-retest
> Standard deviation (using Nakagawa et al. 2015 approach) of test-retest
> Intraclass correlation (transformed to z fisher values) of test-retest
> Pearson correlation coefficient (transformed to z fisher values) derived
> from the same test against criterion measure.
> Because many studies meeting inclusion criteria provided more than one
> effect size through various ways of repeated measures (for example,
> multiple intensities of the test, repeated measures across the year), which
> all based on a common sample of participants, I treated each unique sample
> as an independent study (NOTE: this approach serves our purposes on the
> best way and adding further level will results in low number of
> clusters–which I don't want, given the use of RVE).
> Thanks to the great discussions in this group, we've done the following:
> (1) used rma.mv() to estimate the overall average estimate and the
> variance in hierarchical working model. The same for meta-regressions we
> performed.
> (2) Compute robust variance estimates with robust() and coef_test()
> functions, clustering at the level of studies (the same is true for both
> overall models and meta-regression).
> However, after reading some threads in the groups in the last weeks
> https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2021-January/002565.html
> https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2018-February/000647.html
> and more...I think that one step further is to provide variance-covariance
> matrices for each meta-analysis before step 1 and 2 noted above.
> In this regard I have some other questions:
> (1) Is it compulsory to create (an estimate) variance-covariance given the
> structure of my dataset?
> (2) IF YES, I'm not sure if I can use the same covariance formulas for all
> effect sizes. For example, impute_covariance_matrix() from clubSandwich can
> work fine with all effect sizes (mean diff, SD, icc etc.)? or should I
> estimate the covariance-matrix with a unique function for each effect size?
> Based on reading and suggestions:
> • I used impute_covariance_matrix() for mean difference.
> • For standard deviation I constructed the formula below:
> calc.v <- function(x) {
> v <- matrix(r^2/(2*x$ni[1]-1), nrow=nrow(x), ncol=nrow(x))
> diag(v) <- x$vi
> v
> }
> V <- bldiag(lapply(split(dat, dat$study), calc.v))
> http://www.metafor-project.org/doku.php/analyses:gleser2009
> • for icc and pearson correlation I've looked at this
> https://wviechtb.github.io/metafor/reference/rcalc.html but I couldn't
> create something which is appropriate to my dataset (I don't really know
> how to specify var1 and var2).
> With this regard, I created a sensitivity analysis (with 0.3, 0.5, 0.7 and
> 0.9) which revealed similar overall estimates (also similar to the working
> models without covariance-matrix), albeit, changed a bit the magnitude of
> sigma2.1 and sigma2.2
> I'll be thankful to get any thoughts..
> Kind regards and thanks in advance!
> Tzlil
> Tzlil Shushan | Sport Scientist, Physical Preparation Coach
> BEd Physical Education and Exercise Science
> MSc Exercise Science - High Performance Sports: Strength &
> Conditioning, CSCS
> PhD Candidate Human Performance Science & Sports Analytics

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