[R-meta] multilevel meta-analysis using metafor
Viechtbauer Wolfgang (SP)
wolfgang.viechtbauer at maastrichtuniversity.nl
Wed Sep 6 18:33:44 CEST 2017
As I just mentioned in another post, in models with more complex random effects structures, there are not just weights, but an entire weight matrix. For example, in the model you fitted below, the marginal var-cov matrix is block diagonal with vi + sigma^2_sample_nr + sigma^2_effect_nr along the diagonal and sigma^2_sample_nr for off diagonal elements that correspond to the same level of that factor. The weight matrix is then the inverse of that marginal var-cov matrix. If the sampling errors had been independent, then this would be the 'best' weight matrix (in the sense of giving you the most efficient estimates of the fixed effects and giving you appropriate estimates of the standard errors) and there would then be no need for any further adjustments, manually or statistically.
In your case, the sampling errors are correlated, but you are fitting the model with V=vi, that is, assuming a diagonal V matrix. That will lead to less efficient but still unbiased estimates of the fixed effects, but invalid estimates of the standard errors, and hence the need for using robust(model, cluster=data$sample_nr). But this doesn't actually change the weight matrix; it only affects the way the var-cov matrix of the fixed effects is computed and hence the computation of the standard errors. So, also here there is no need to manually adjust any weights.
And yes, *all else equal*, studies with more effect sizes are weighted more than studies with less effect sizes. Let me put this in a different way: A study providing more evidence about the phenomenon under investigation is getting more weight than a study providing less evidence. That seems pretty sensible to me.
From: brauldeq [mailto:brauldeq at hu-berlin.de]
Sent: Tuesday, 05 September, 2017 13:25
To: Viechtbauer Wolfgang (SP)
Cc: r-sig-meta-analysis at r-project.org
Subject: Re: [R-meta] multilevel meta-analysis using metafor
I was wondering how the weights are assigned correctly when specifying
rma.mv(yi, vi, random = ~ 1 | sample_nr/effect_nr, data = data) and
robust.rma.mv(model, cluster=data$sample_nr, adjust=T) for a (cluster)
robust estimation of variances and accurate assessment of standard
Just a little reminder concerning my data structure. I have multiple
effect sizes (all measured in the same sample) per study. The number of
effect sizes within one study varies greatly from only 1 up until 30.
I am asking because not long ago I was quite certain that by specifying
a two-level random effects model, the dependency of effect sizes within
studies would automatically be accounted for. Now I know that this is
not the case and I need to account for the dependency of standard errors
additionally with the robust() function. However, now I am wondering
whether the weights are assigned correctly with the approach explained
above. Or do I need to adjust the weights of studies with multiple
(dependent) effect sizes manually? Specifically, do studies with more
effect sizes are weighted more than studies with less effect sizes? This
would be very problematic in my case.
Thanks for providing me with more details concerning the weighting of
effect sizes within two-level random-effect models using the rma.mv()
Am 30.08.2017 16:31, schrieb Wolfgang Viechtbauer:
> Yes, this is indeed how you can approach this.
> And yes, if the var-cov structured is misspecified (which it is in
> your case), then the fixed effects are still estimated unbiasedly
> (although not as efficiently). The problem is that the SEs of the
> fixed effects will not be correct. Using robust() allows you to get
> more appropriate estimates of the SEs (and hence more appropriate
> On 08/30/2017 03:10 PM, brauldeq wrote:
> I understand that I could at first specify my model using model <-
> rma.mv(yi, vi, random = ~ 1 | sample_nr/effect_nr, data = data). To
> solve the problem concerning the covariances of the sampling errors I
> would hereafter use robust.rma.mv(model, cluster=data$sample_nr, adjust
> = T). Would this approach solve my problem?
> Am I right to assume that the rma.mv(yi, vi, random = ~1 |
> sample_nr/effect_nr) function would calculate proper estimate of effect
> size but is problematic in terms of sampling errors?
> Am 30.08.2017 14:05, schrieb Wolfgang Viechtbauer:
> Please keep the mailing list in cc.
> Yes, this means the subjects overlap, that is, the correlations are
> computed based on the same sample. In that case, the correlations are
> correlated. Equations for computing the covariances can be found in:
> Steiger, J. H. (1980). Tests for comparing elements of a correlation
> matrix. Psychological Bulletin, 87(2), 245-251.
> There are various cases. Let's say there are four variables: x1, x2,
> x4, and x4, all measured in the same sample. Then we have the case of
> non-overlapping variables:
> cov(cor(x1,x2), cor(x3,x4))
> To compute that covariance, you will need the full 4x4 correlation
> And there is the case of partially overlapping variables, for example:
> cov(cor(x1,x2), cor(x1,x3))
> To compute the covariance here, you will need cor(x2,x3) (obviously,
> cor(x1,x2) and cor(x1,x3) you already have, otherwise you would not be
> interested in their covariance).
> Again, the necessary equations can be found in Steiger (1980).
> If you do not have the information to compute the covariances, then we
> are back to the situation where the covariances between the outcomes
> cannot be computed. See previous posts on how to deal with that. For
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