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The Impact of Excluding Trials from Network Meta-Analyses - An Empirical Study

dc.contributor.authorZhang, Jing
dc.contributor.authorYuan, Yiping
dc.contributor.authorChu, Haitao
dc.date.accessioned2017-08-31T19:54:26Z
dc.date.available2017-08-31T19:54:26Z
dc.date.issued2016-12-07
dc.identifierhttps://doi.org/10.13016/M29S1KK69
dc.identifier.citationZhang J, Yuan Y, Chu H (2016) The Impact of Excluding Trials from Network Meta- Analyses ± An Empirical Study. PLoS ONE 11(12): e0165889. doi:10.1371/journal.pone.0165889en_US
dc.identifier.urihttp://hdl.handle.net/1903/19711
dc.descriptionPartial funding for Open Access provided by the UMD Libraries' Open Access Publishing Fund.en_US
dc.description.abstractNetwork meta-analysis (NMA) expands the scope of a conventional pairwise meta-analysis to simultaneously compare multiple treatments, which has an inherent appeal for clinicians, patients, and policy decision makers. Two recent reports have shown that the impact of excluding a treatment on NMAs can be substantial. However, no one has assessed the impact of excluding a trial from NMAs, which is important because many NMAs selectively include trials in the analysis. This article empirically examines the impact of trial exclusion using both the arm-based (AB) and contrast-based (CB) approaches, by reanalyzing 20 published NMAs involving 725 randomized controlled trials and 449,325 patients. For the population-averaged absolute risk estimates using the AB approach, the average fold changes across all networks ranged from 1.004 (with standard deviation 0.004) to 1.072 (with standard deviation 0.184); while the maximal fold changes ranged from 1.032 to 2.349. In 12 out of 20 NMAs, a 1.20-fold or larger change is observed in at least one of the population- averaged absolute risk estimates. In addition, while excluding a trial can substantially change the estimated relative effects (e.g., log odds ratios), there is no systematic difference in terms of changes between the two approaches. Changes in treatment rankings are observed in 7 networks and changes in inconsistency are observed in 3 networks. We do not observe correlations between changes in treatment effects, treatment rankings and inconsistency. Finally, we recommend rigorous inclusion and exclusion criteria, logical study selection process, and reasonable network geometry to ensure robustness and generalizability of the results of NMAs.en_US
dc.language.isoen_USen_US
dc.publisherPLoS (Public Library of Science)en_US
dc.titleThe Impact of Excluding Trials from Network Meta-Analyses - An Empirical Studyen_US
dc.typeArticleen_US
dc.relation.isAvailableAtEpidemiology & Biostatistics
dc.relation.isAvailableAtSchool of Public Health
dc.relation.isAvailableAtDigital Repository at the University of Maryland (DRUM)
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)


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