[R-meta] Multivariate meta-analysis with unknown covariances?
Viechtbauer Wolfgang (SP)
wolfgang.viechtbauer at maastrichtuniversity.nl
Thu Aug 10 21:23:50 CEST 2017
Indeed, unknown correlations seems to be a 'hot topic' right now.
Let me first clarify that metafor cannot somehow magically solve the problem with missing covariances. One has to do some extra work to deal with this issue. The post that Isabel refers to (https://stat.ethz.ch/pipermail/r-sig-mixed-models/2015q2/023727.html) discusses some possibilities. A defensible strategy is to:
1) Guestimate the unknown correlations and then compute approximate covariances between the effect size estimates.
2) Fit a proper multivariate model.
3) Follow things up with a cluster-robust approach.
This is basically what James just described in his post and on his blog (and what Emily refers to):
And so, yes, I think you can conduct a multivariate meta-analysis based on the data set described.
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On Behalf Of Emily Finne
Sent: Thursday, August 10, 2017 20:25
To: r-sig-meta-analysis at r-project.org
Subject: Re: [R-meta] Multivariate meta-analysis with unknown covariances?
I'm not an expert in meta-analysis, so I have to leave your question -
if multivariate MA makes sense at all in your case - open to the experts
around. If your different outcomes do measure different symptoms
originating in different disorders I would have my doubts if it makes
sense to combine them.
But it seems to me that the follwing recent post is addressing your
problem with the missing covariances quite well:
How to construct the var-cov matrix for multiple endpoint studies in
metafor is illustrated here:
http://www.metafor-project.org/doku.php/analyses:gleser2009. (But you
would have to guess a within-study correlation between different outcomes).
Good luck with your thesis!
Am 10.08.2017 um 18:54 schrieb schlegei:
> for my master thesis, I want to conduct a multivariate meta-analysis
> with the R-package metafor.
> Unfortunately, I‘m not sure if it is possible to conduct this analysis
> based on my data set.
> To illustrate my problem, a few words about my research question: I
> investigate the efficacy of gestalt therapy (a psychotherapeutic
> approach) for people with a mental disorder according to DSM-IV/ICD-10
> (my focus is on symptom reduction). My literature research resulted in
> 12 randomized controlled trials (RCTs).
> From this 12 studies, I extracted 28 outcomes and calculated the
> effect sizes and variances (standardized mean differences). I assume
> outcomes from the same study are dependent. Unfortunately, in no study
> correlations/covariances between the sampling errors of the outcomes
> are reported. So the within study covariance structure is totally
> Because I included studies about people with different mental
> disorders, my outcomes are pretty heterogeneous: Only one
> questionnaire (Beck‘s Depression Inventory) was used in 4 studies,
> apart from that the outcomes don‘t overlap between the studies.
> Summed up, my data set consists of the following variables:
> ES_ID = idenfification number for every effect size (I have 28 effect
> study = every study gets one number (I included 12 studies)
> outcome = every questionnaire/outcome gets one letter (I included 24
> different outcomes)
> yi = effect size
> vi = variance of the effect size
> Is it possible to conduct a multivariate meta-analysis based on this
> data set?
> My supervisor told me, the missing within study covariances can be
> easily estimated with the R-package metafor. Up until now, I do not
> understand how. Following the discussions on stackexchange and this
> mailing list (e.g.
> it seems to me that estimating the whole covariance structure is not
> that easy and is attended by some disadvantages/assumptions . This
> also coincides with other articles I have read about multivariate
> meta-analysis and in which missing covariances are described as a
> major problem. When I contacted my supervisor, he just told me that
> the literature I read is out-dated and repeated that the problem of
> missing covariances can be solved with metafor (unfortunately, he
> didn‘t recommend up-to-date articles to me).
> Right now, I feel a bit locked in a stalemate. Is there a simple,
> up-to-date solution for my problem with the missing covariances that I
> have overseen?
> If yes: I would be extremely happy about any tip!
> If no: Do you think, it makes sense to conduct a multivariate
> meta-analysis based on my data set or is it more appropriate to choose
> one effect size per study (univariate meta-analysis)? If it is
> possible to conduct a multivariate meta-analysis based on my data: Is
> there a strategy – like making a rough guess of the correlations or
> using robust methods – that you would recommend?
> I would be really happy to hear a response,
> Isabel Schlegel
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