Visualizing Assumptions and Results in Network Meta-analysis: The Network Graphs Package
By A. Chaimani, Georgia Salanti. 2015
295 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/d0b54aad74b917a49dd441c3a74679b00ca7c3b2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
In Experimental and Cognitive Psychology
- Ian R. White
- A. Chaimani
- Georgia Salanti
- Julian P. T. Higgins
- A. Sutton
- R. Owen
- N. Cooper
- Naomi V. Bradbury
In Statistics and Probability
- Georgia Salanti
- A. Chaimani
- Ian R. White
- Julian P. T. Higgins
- A. Sutton
- Adriani Nikolakopoulou
- Theodoros Papakonstantinou
- Matthias Egger
Citations in the corpus
- CINeMA: An approach for assessing confidence in the results of a network meta-analysis (2020, result): “• Empirical examination of patterns of results between small and large studies, using comparison-adjusted [35,36], regression [37], or selection [38] models, do not indicate that results from small studies differ from those in published studies.”
- Network Meta-analysis (2015, background): “Various parts of the network package require mvmeta version 3.1 or greater (White 2009, 2011, 2015), metareg (Harbord and Higgins 2008), and networkplot version 1.2 or greater (Chaimani et al. 2013; Chaimani and Salanti 2015).”
- MetaInsight: An interactive web‐based tool for analyzing, interrogating, and visualizing network meta‐analyses using R‐shiny and netmeta (2019, methodology): “Technical users may wish to make use of the full flexibility, modeling options, and output offered in the netmeta package 10 as well as other packages such as metafor 15 in R, network 16 and network_graphs 17 in Stata, SAS, JAGS, WinBUGS, OpenBUGS, and Stan.”