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
sizes)
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.
https://stat.ethz.ch/pipermail/r-sig-mixed-models/2015q2/023727.html),
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