In network meta-analysis most of the information comes from indirect evidence: empirical study.

Papakonstantinou, Theodoros; Nikolakopoulou, Adriani; Egger, Matthias; Salanti, Georgia (2020). In network meta-analysis most of the information comes from indirect evidence: empirical study. Journal of clinical epidemiology, 124, pp. 42-49. Elsevier 10.1016/j.jclinepi.2020.04.009

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OBJECTIVE Network meta-analysis (NMA) may produce more precise estimates of treatment effects than pairwise meta-analysis. We examined the relative contribution of network paths of different lengths to estimates of treatment effects. STUDY DESIGN AND SETTING We analyzed 213 published NMAs. We categorized network shapes according to the presence or absence of at least one closed loop (non-star or star network), and derived graph density, radius and diameter. We identified paths of different lengths and calculated their percentage contribution to each NMA effect estimate, based on their contribution matrix. RESULTS Among the 213 NMAs included in analyses, 33% of the information came from paths of length 1 (direct evidence), 47% from paths of length 2 (indirect paths with one intermediate treatment) and 20% from paths of length 3. The contribution of paths of different lengths depended on the size of networks, presence of closed loops, graph radius, density and diameter. Longer paths contribute more as the number of treatments and loops, the graph radius and diameter increase. CONCLUSION The contribution of different paths depends on the size and structure of networks, with important implications for assessing the risk of bias and confidence in NMA results.

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:

Papakonstantinou, Theodoros; Nikolakopoulou, Adriani; Egger, Matthias and Salanti, Georgia

Subjects:

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

ISSN:

0895-4356

Publisher:

Elsevier

Funders:

[4] Swiss National Science Foundation

Language:

English

Submitter:

Andrea Flükiger-Flückiger

Date Deposited:

23 Apr 2020 19:39

Last Modified:

28 May 2020 06:15

Publisher DOI:

10.1016/j.jclinepi.2020.04.009

PubMed ID:

32302680

Uncontrolled Keywords:

flow decomposition flow networks network meta-analysis network of interventions paths of evidence study contribution

BORIS DOI:

10.7892/boris.143473

URI:

https://boris.unibe.ch/id/eprint/143473

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