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Li, Hua; Shih, Ming-Chieh; Song, Cheng-Jie; Tu, Yu-Kang – Research Synthesis Methods, 2023
Network meta-analysis combines direct and indirect evidence to compare multiple treatments. As direct evidence for one treatment contrast may be indirect evidence for other treatment contrasts, biases in the direct evidence for one treatment contrast may affect not only the estimate for this particular treatment contrast but also estimates of…
Descriptors: Network Analysis, Meta Analysis, Bias, Evidence
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Seitidis, Georgios; Tsokani, Sofia; Christogiannis, Christos; Kontouli, Katerina-Maria; Fyraridis, Alexandros; Nikolakopoulos, Stavros; Veroniki, Areti Angeliki; Mavridis, Dimitris – Research Synthesis Methods, 2023
Network meta-analysis (NMA) is an established method for assessing the comparative efficacy and safety of competing interventions. It is often the case that we deal with interventions that consist of multiple, possibly interacting, components. Examples of interventions' components include characteristics of the intervention, mode (face-to-face,…
Descriptors: Networks, Network Analysis, Meta Analysis, Intervention
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Harari, Ofir; Soltanifar, Mohsen; Cappelleri, Joseph C.; Verhoek, Andre; Ouwens, Mario; Daly, Caitlin; Heeg, Bart – Research Synthesis Methods, 2023
Effect modification (EM) may cause bias in network meta-analysis (NMA). Existing population adjustment NMA methods use individual patient data to adjust for EM but disregard available subgroup information from aggregated data in the evidence network. Additionally, these methods often rely on the shared effect modification (SEM) assumption. In this…
Descriptors: Networks, Network Analysis, Meta Analysis, Evaluation Methods
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Luo, Yan; Chaimani, Anna; Furukawa, Toshi A.; Kataoka, Yuki; Ogawa, Yusuke; Cipriani, Andrea; Salanti, Georgia – Research Synthesis Methods, 2021
It is often challenging to present the available evidence in a timely and comprehensible manner. We aimed to visualize the evolution of evidence about antidepressants for depression by conducting cumulative network meta-analyses (NMAs) and to examine whether it could have helped the selection of optimal drugs. We built a Shiny web application that…
Descriptors: Networks, Network Analysis, Meta Analysis, Drug Therapy
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Seo, Michael; Furukawa, Toshi A.; Veroniki, Areti Angeliki; Pillinger, Toby; Tomlinson, Anneka; Salanti, Georgia; Cipriani, Andrea; Efthimiou, Orestis – Research Synthesis Methods, 2021
Network meta-analysis (NMA) can be used to compare multiple competing treatments for the same disease. In practice, usually a range of outcomes is of interest. As the number of outcomes increases, summarizing results from multiple NMAs becomes a nontrivial task, especially for larger networks. Moreover, NMAs provide results in terms of relative…
Descriptors: Networks, Network Analysis, Meta Analysis, Visualization
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Seide, Svenja E.; Jensen, Katrin; Kieser, Meinhard – Research Synthesis Methods, 2021
Traditional visualization in meta-analysis uses forest plots to illustrate the combined treatment effect, along with the respective results from primary trials. While the purpose of visualization is clear in the pairwise setting, additional treatments broaden the focus and extend the results to be illustrated in network meta-analysis. The…
Descriptors: Graphs, Visualization, Simulation, Meta Analysis
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Noma, Hisashi; Gosho, Masahiko; Ishii, Ryota; Oba, Koji; Furukawa, Toshi A. – Research Synthesis Methods, 2020
Network meta-analysis has been gaining prominence as an evidence synthesis method that enables the comprehensive synthesis and simultaneous comparison of multiple treatments. In many network meta-analyses, some of the constituent studies may have markedly different characteristics from the others, and may be influential enough to change the…
Descriptors: Networks, Meta Analysis, Evidence, Comparative Analysis
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Zhao, Hong; Hodges, James S.; Carlin, Bradley P. – Research Synthesis Methods, 2017
Network meta-analysis (NMA) combines direct and indirect evidence comparing more than 2 treatments. Inconsistency arises when these 2 information sources differ. Previous work focuses on inconsistency detection, but little has been done on how to proceed after identifying inconsistency. The key issue is whether inconsistency changes an NMA's…
Descriptors: Meta Analysis, Networks, Hierarchical Linear Modeling, Bayesian Statistics