Theodoros Papakonstantinou1, Adriani Nikolakopoulou2, Matthias Egger3, Georgia Salanti2. 1. Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland. Electronic address: theodoros.papakonstantinou@ispm.unibe.ch. 2. Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland. 3. Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
Abstract
OBJECTIVES: 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 (nonstar or star network) and derived the 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, and graph radius, density, and diameter. Longer paths contribute more as the number of treatments and loops and 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.
OBJECTIVES: 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 (nonstar or star network) and derived the 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, and graph radius, density, and diameter. Longer paths contribute more as the number of treatments and loops and 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.
Authors: Maria Neves Carmona; Hugo Santos-Sousa; Luís Lindeza; Bernardo Sousa-Pinto; Jorge Nogueiro; André Pereira; Silvestre Carneiro; André Costa-Pinho; Eduardo Lima-da-Costa; John Preto Journal: Obes Surg Date: 2021-10-05 Impact factor: 4.129
Authors: Ian Leigh Alberts; Svenja Elizabeth Seide; Clemens Mingels; Karl Peter Bohn; Kuangyu Shi; Helle D Zacho; Axel Rominger; Ali Afshar-Oromieh Journal: Eur J Nucl Med Mol Imaging Date: 2021-02-06 Impact factor: 9.236