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
[R-meta] Multivariate meta-analysis with unknown covariances?
1 message · schlegei