Multivariate and network meta-analysis of multiple outcomes and multiple treatments: rationale, concepts, and examples

Summary points:
• Meta-analysis methods combine quantitative evidence from related studies to
produce results based on a whole body of research
• Studies that do not provide direct evidence about a particular outcome or
treatment comparison of interest are often discarded from a meta-analysis of
that outcome or treatment comparison
• Multivariate and network meta-analysis methods simultaneously analyse
multiple outcomes and multiple treatments, respectively, which allows more
studies to contribute towards each outcome and treatment comparison
• Summary results for each outcome now depend on correlated results from
other outcomes, and summary results for each treatment comparison now
incorporate indirect evidence from related treatment comparisons, in addition
to any direct evidence
• This often leads to a gain in information, which can be quantified by the
“borrowing of strength” statistic, BoS (the percentage reduction in the
variance of a summary result that is due to correlated or indirect evidence)
• Under a missing at random assumption, a multivariate meta-analysis of
multiple outcomes is most beneficial when the outcomes are highly correlated
and the percentage of studies with missing outcomes is large
• Network meta-analyses gain information through a consistency assumption,
which should be evaluated in each network where possible. There is usually
low power to detect inconsistency, which arises when effect modifiers are
systematically different in the subsets of trials providing direct and indirect
evidence
• Network meta-analysis allows multiple treatments to be compared and ranked
based on their summary results. Focusing on the probability of being ranked
first is, however, potentially misleading: a treatment ranked first may also have
a high probability of being ranked last, and its benefit over other treatments
may be of little clinical value
• Novel network meta-analysis methods are emerging to use individual
participant data, to evaluate dose, to incorporate “real world” evidence from
observational studies, and to relax the consistency assumption by allowing
summary inferences while accounting for inconsistency effects