I have a dataset that I want to run a meta-analysis on. However, the data has a hierarchical structure with several endpoints per study and where studies are further clustered together. So, I should run a multilevel model to account for this.
metafor manual pages for
rma.mv reveals that you can input a
vector of length k with the corresponding sampling variances or a k×k variance-covariance matrix of the sampling errors.
In my case, I don't have the variance-covariance matrix and there's absolutely no way for me to find out what it is, so I'm just supplying the function with a vector of sampling variances.
Now, I'm trying to understand what practical effect this will have on the outcome of my analysis. Can/should I still run the model or do I need a variance-covariance matrix in order for it to make sense?