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) |
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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, 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: |
05 Dec 2022 15:38 |
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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