[R-meta] Multivariate meta-analysis with unknown covariances?
schlegei at hu-berlin.de
Thu Aug 10 18:54:28 CEST 2017
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 missing.
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
yi = effect size
vi = variance of the effect size
Is it possible to conduct a multivariate meta-analysis based on this
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
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,
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