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Network Meta-Analysis

Series editor: Areti Angeliki Veroniki Network meta-analysis provides a thorough and powerful method to assess the effectiveness of multiple interventions simultaneously in a single analysis by combining evidence from a network of trials. In clinical fields where scientific evidence accrues exponentially and several competing treatments are available, network meta-analysis has become an established tool to inform evidence-based decisions. This massive increase in scientific publications has also resulted in the development of novel methods for collecting, synthesizing and interpreting the available evidence. More advancements are expected to arise in the following years probably with an increased focus on reporting, reproducibility, assessment of and modelling within and across study biases, credibility and interpretation of the network meta-analysis results. These are particularly challenging in large and heterogenous evidence bases. Similarly, methodological developments are expected in the modelling of diagnostic test accuracy studies in network meta-analysis to determine and rank the optimal diagnostic tests and relevant thresholds for the diagnosis of a particular disease. Retrieval barriers in individual participant data reviews with network meta-analysis Knowledge user survey and Delphi process to inform development of a new risk of bias tool to assess systematic reviews with network meta-analysis (RoB NMA tool) Using individual participant data to improve network meta-analysis projects Component network meta-analysis in a nutshell Theory and practice of Bayesian and frequentist frameworks for network meta-analysis The complexity underlying treatment rankings: how to use them and what to look at Implementing hierarchical network meta-analysis incorporating exchangeable dose effects compared to standard hierarchical network meta-analysis