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

Riley, Richard D; Jackson, Dan; Salanti, Georgia; Burke, Danielle L; Price, Malcolm; Kirkham, Jamie; White, Ian R (2017). Multivariate and network meta-analysis of multiple outcomes and multiple treatments: rationale, concepts, and examples. BMJ, 358, j3932. BMJ Publishing Group 10.1136/bmj.j3932

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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

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Social and Preventive Medicine (ISPM)

UniBE Contributor:

Salanti, Georgia

Subjects:

600 Technology > 610 Medicine & health
300 Social sciences, sociology & anthropology > 360 Social problems & social services

ISSN:

1756-1833

Publisher:

BMJ Publishing Group

Language:

English

Submitter:

Tanya Karrer

Date Deposited:

26 Oct 2017 09:57

Last Modified:

01 Nov 2017 14:53

Publisher DOI:

10.1136/bmj.j3932

PubMed ID:

28903924

BORIS DOI:

10.7892/boris.106479

URI:

https://boris.unibe.ch/id/eprint/106479

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