[R-meta] imputing covariance matrices for meta-analysis of dependent effects

James Pustejovsky jepusto at gmail.com
Thu Aug 10 17:59:21 CEST 2017


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.


On Thu, Aug 10, 2017 at 10:43 AM, Michael Dewey <lists at dewey.myzen.co.uk>

> 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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