I have multiple outcome measures (HDL, LDL, TG & Total cholesterol) that are measured before, at the end, and a month after an intervention and I would like to perform meta-analysis.
Multivariate meta-analysis is an extension of the classical meta-analysis with one outcome to more than one outcomes. Multivariate meta-analysis allows you to incorporate the correlation that might be present among multiple outcomes.
Network meta-analysis extends the classical meta-analysis with one relative treatment effect to more than one relative treatment effects on the same outcome as long there are network is connected. E.G suppose there a n1 studies comparing A-B treatment effect and n2 studies comparing A-C treatment effect and no studies have studied B-C treatment effect, then a network meta-analysis would enable you to estimate it indirectly.
At the relative-effects levels, network meta-analysis can be thought of as multivariate meta-analysis where some of the treatment-effects are missing at random.
See Ian R. W 2009. Multivariate random-effects meta-analysis with Stata
Ian R. W 2012. Consistency and inconsistency in network meta-analysis: model estimation using multivariate meta-regression
Your data seem amenable to a standard meta-analysis as there is a pre-post element and you may want to compute a mean gain.
In terms of network meta-analyses, there are three frameworks through which a such a meta-analysis is performed:
a)Bayesian modelling
b)Multivariate frequentist modelling
c)Generalised pairwise modelling
The last is a new approach that is very simple and straightforward for researchers and a free software is available to implement this (MetaXL) at www.epigear.com
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