[R-meta] Multi-level model accounting for within-cluster correlation
James Pustejovsky
jepu@to @end|ng |rom gm@||@com
Mon Jun 19 19:49:51 CEST 2023
Hi Guido,
The difference between your res1 and res2 is the assumption about the
sampling correlation of effect size estimates nested within clusters. Say
that you've got effect size estimate ES_ij for effect i nested in cluster
j, which is an estimate of the true effect delta_ij. Your res1 makes the
assumption that cor(ES_hj - delta_hj, ES_ij - delta_ij) = 0.6 for all h,i
in a given cluster and then for all clusters. Your res2 makes the
assumption that this correlation is zero. We can think of these as two
different "working models," neither of which is necessarily correct.
Here is my understanding of the properties of the estimators from each
working model:
1. Under either assumption, the point-estimate of the average effect across
clusters will be unbiased. The point-estimate of the average effect will be
more efficient to the extent that the working model is closer to the true
data-generating process.
2. The model-based standard errors generated by metafor might be off (to
some extent) if the working model is incorrect, but this can be fixed by
using robust variance estimation methods:
robust(res1, cluster = cluster_id, clubSandwich = TRUE)
robust(res2, cluster = cluster_id, clubSandwich = TRUE)
3. The variance component estimates of either model can be biased to some
extent if the working model is incorrect. Roughly speaking, the variance
component estimates will be less biased when the working model is closer to
correct.
In light of the above, it seems to me that the thing to do is pick
whichever working model you find more plausible as a representation of the
real data-generating process. If it's more plausible that there should be
positive sampling correlation between the effect size estimates than that
there is no correlation, then I would go with res1 plus robust variance
estimation.
All that said, it might be good to explain a bit more about the data
structure here. It looks like you've got effect size estimates nested in
study IDs nested in cluster IDs. Are there multiple effect size estimates
per study ID? Or only one? If only one, then why would you expect there to
be correlation between effect size estimates from different studies (even
if from the same cluster)?
James
On Fri, Jun 16, 2023 at 9:57 AM Dr. Guido Schwarzer via R-sig-meta-analysis
<r-sig-meta-analysis using r-project.org> wrote:
> Hi,
>
> In statistical consulting, a Master's student asked me whether the
> following R code is correct to conduct a multi-level meta-analysis:
>
> ## assume that the effect sizes within studies are correlated with rho =
> 0.6
> V <- vcalc(vi, cluster = cluster_id, obs = study_id, data = dat, rho = 0.6)
> ## fit multilevel model using this approximate V matrix
> res1 <- rma.mv(yi, V, random = ~ 1 | cluster_id / study_id, data = dat)
>
> To my understanding, the advantage of a multi-level model is that no
> assumption on the within-cluster correlation is required / the correlation
> must no be specified, i.e., the model would be
> res2 <- rma.mv(yi, vi, random = ~ 1 | cluster_id / study_id, data = dat)
>
> Am I correct?
>
> And, if so, does the above model using the block diagonal covariance
> matrix V make any sense?
>
> Best,
> Guido
>
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