[R-meta] imputing covariance matrices for meta-analysis of dependent effects
James Pustejovsky
jepusto at gmail.com
Thu Aug 10 21:33:47 CEST 2017
Wolfgang,
Thanks for your thoughts. I agree that the covariance formula I'm using is
an approximation, and would be most appropriate for use in conjunction with
cluster-robust variance estimation. It might be more accurate to describe
this method as imputing a correlation between the effect size estimates,
rather than a correlation between the outcomes. In practice, I doubt that
there will be much difference though, particularly considering that the
formulas given in Gleser & Olkin and Kalaian & Raudenbush are themselves
only large-sample approximations.
Regarding your concern about using three-level models in this context, I
have seen this method cropping up recently as well, with citations to the
following paper:
> Van den Noortgate, W., López-López, J. A., Marín-Martínez, F., &
> Sánchez-Meca, J. (2013). Three-level meta-analysis of dependent effect
> sizes. Behavior Research Methods, 45(2), 576–594.
> https://doi.org/10.3758/s13428-012-0261-6
The authors argue that the three-level model is actually robust to the
mis-specification problem you noted. However, the simulation evidence that
they present is limited to a simple bivariate meta-analysis model with no
covariates. I am not sure whether the robustness property would hold under
more complicated models.
James
On Thu, Aug 10, 2017 at 2:11 PM, Viechtbauer Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
> Hi James,
>
> This is indeed useful.
>
> However, I am not sure if you are computing the covariances correctly. You
> are essentially using:
>
> covariance = correlation * sqrt(variance1 * variance2)
>
> But apparently, if one goes through the derivation for the covariance for
> standardized mean differences, one ends up with a different equation
> (equation 10 in Kalaian and Raudenbush, 1996). In fact, the equation for
> the covariance depends on the measure used. See, for example:
>
> Wei, Y., & Higgins, J. P. (2013). Estimating within-study covariances in
> multivariate meta-analysis with multiple outcomes. Statistics in Medicine,
> 32(7), 1191-1205.
>
> Historical side note: Interestingly, the 'covariance = correlation *
> sqrt(variance1 * variance2)' equation for standardized mean differences was
> also used in:
>
> Raudenbush, S. W., Becker, B. J., & Kalaian, H. (1988). Modeling
> multivariate effect sizes. Psychological Bulletin, 103, 111-120.
>
> but this was later corrected in Kalaian and Raudenbush (1996) based on
> Gleser and Olkin (1994).
>
> In practice, it probably makes relatively difference how exactly one
> computes those covariances, esp. if one is 'guestimating' the correlation
> between the measures anyway (and then follows things up with some kind of
> cluster-robust approach, as you describe on your blog). As far as I am
> concerned, it is important though that one actually computes some kind of
> covariances (to get a better 'working' var-cov matrix to begin with). I am
> seeing an increasing number of papers where multiple effect size estimates
> based on the same sample (so, multivariate data) are being analyzed using a
> multilevel model like the one described by Konstantopoulos (2011). But that
> model assumes that sampling errors are uncorrelated, so this is a
> misapplication of that model. That is also why I added this at one point:
>
> http://www.metafor-project.org/doku.php/analyses:konstantopoulos2011#
> uncorrelated_sampling_errors
>
> As for the different results reported in Kalaian and Raudenbush (1996) and
> the ones obtained with metafor -- a couple years ago, I also entered those
> data (from Table 1) and tried to re-analyze the dataset (as I was planning
> to include that dataset also in metafor) and ran into similar
> discrepancies. In fact, if one re-creates the scatterplots that are shown
> in Figure 1 from the data reported in Table 1, it becomes clear that there
> must be several printing errors. So, it's not really possible to reproduce
> the results from that paper, which is unfortunate, since it would be a nice
> illustration of the multivariate approach.
>
> Best,
> Wolfgang
>
> --
> Wolfgang Viechtbauer, Ph.D., Statistician | Department of Psychiatry and
> Neuropsychology | Maastricht University | P.O. Box 616 (VIJV1) | 6200 MD
> Maastricht, The Netherlands | +31 (43) 388-4170 | http://www.wvbauer.com
>
> -----Original Message-----
> From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-
> bounces at r-project.org] On Behalf Of James Pustejovsky
> Sent: Thursday, August 10, 2017 17:59
> To: Michael Dewey
> Cc: r-sig-meta-analysis at r-project.org
> Subject: Re: [R-meta] imputing covariance matrices for meta-analysis of
> dependent effects
>
> Michael,
>
> I was not aware of the metavcov package, so thank you for pointing it out.
>
> On first glance, it looks like the metavcov functions are configured based
> on the assumption that you have detailed information about the correlations
> between outcomes for each study (i.e., it requires a list of correlation
> matrices as input). The function from my previous message is a simpler
> utility function, for use when you need to make more or less ad hoc
> assumptions about the correlations. So I would say that it does complement
> the metavcov package, but I would welcome corrections if this is not an
> accurate assessment.
>
> Best,
> James
>
> On Thu, Aug 10, 2017 at 10:43 AM, Michael Dewey <lists at dewey.myzen.co.uk>
> wrote:
>
> > Dear James
> >
> > Not sure how relevant this is but does it complement in any way the
> > package https://CRAN.R-project.org/package=metavcov ? I have not used
> it
> > by the way.
> >
> > Michael
> >
> > On 10/08/2017 15:04, James Pustejovsky wrote:
> >
> >> All,
> >>
> >> A common problem in multivariate meta-analysis is that the information
> >> needed to calculate the correlation between effect size estimates is not
> >> reported in available sources, even when the variances of the estimates
> >> can
> >> be calculated. One approach to handling this situation is to simply make
> >> an
> >> informed guess about the correlation between the effect sizes. I use
> this
> >> approach fairly often and have written a function that makes some of the
> >> calculations easier. The function calculates a block-diagonal
> >> variance-covariance matrix based on the sampling variances and a guess
> >> about the degree of correlation. More details available here:
> >>
> >> http://jepusto.github.io/imputing-covariance-matrices-for-
> >> multi-variate-meta-analysis
> >>
> >> There's nothing innovative about the methods I describe, but I figured
> >> that
> >> others might find the function useful. I would welcome comments,
> >> questions,
> >> or debate about the utility of the approach I used.
> >>
> >> Cheers,
> >> James
>
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