[R-meta] Credibility intervals with RVE

James Pustejovsky jepu@to @end|ng |rom gm@||@com
Tue Apr 9 22:25:16 CEST 2019


a) Your approach is exactly what I would do (and have done in the past).
b) You are exactly right about the limitations of the approach. With RVE,
it's recommended to conduct sensitivity analyses. You could do the same
thing here, and report how the prediction interval changes depending on the
assumed value of r.


On Tue, Apr 9, 2019 at 1:44 PM Fabian Schellhaas <fabian.schellhaas using yale.edu>

> Hi all,
> Credibility intervals can be useful because – in contrast to confidence
> intervals – they consider the heterogeneity of synthesized effects and make
> predictions about the range of values within which the true effect of
> future studies will likely fall (e.g., IntHout et al., 2016).
> It is straightforward enough to calculate credibility intervals for rma and
> rma.mv objects in metafor using the `predict` function. However, (a) how
> would one compute credibility intervals for multivariate multilevel
> meta-analytic models with cluster-robust variance estimation, and (b) is
> doing so even meaningful, given that the covariance matrix has only been
> aproximated? I included more details about both questions below.
> Thanks a lot!
> Fabian
> -----
> Regarding question (a), I would compute the credibility interval for a
> simplified example as follows:
> # fit model
> vcv <- clubSandwich::impute_covariance_matrix(vi = data$vi, cluster =
> data$cluster_id, r = 0.7)
> m <- metafor::rma.mv(yi, vcv, random = ~ 1 | cluster_id/es_id, data =
> data)
> robu <- clubSandwich::coef_test(m, vcov = "CR2")
> # compute 95% credibility interval
> robu$beta - 1.96*sqrt(sum(m$sigma2) + robu$SE^2)
> robu$beta + 1.96*sqrt(sum(m$sigma2) + robu$SE^2)
> Regarding question (b), since V in this model is just a rough approximation
> of the empirical covariance structure, the variance components of the model
> are also just an approximation as well. Even when using cluster-robust
> standard errors for the computation of the credibility intervals, we still
> use the model's approximated variance components, and thus the credibility
> interval provides just a rough guess of the range within which future
> studies will fall.
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