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Software and package applicating for network meta‐analysis: A usage‐based comparative study
Author(s) -
Xu Chang,
Niu Yuming,
Wu Junyi,
Gu Huiyun,
Zhang Chao
Publication year - 2018
Publication title -
journal of evidence‐based medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.885
H-Index - 22
ISSN - 1756-5391
DOI - 10.1111/jebm.12264
Subject(s) - computer science , software , software engineering , software construction , frequentist inference , software development , data mining , bayesian probability , artificial intelligence , bayesian inference , programming language
Objective To compare and analyze the characteristics and functions of software applications for network meta‐analysis (NMA). Methods PubMed, EMbase, The Cochrane Library, the official websites of Bayesian inference Using Gibbs Sampling (BUGS), Stata and R, and Google were searched to collect the software and packages for performing NMA; software and packages published up to March 2016 were included. After collecting the software, packages, and their user guides, we used the software and packages to calculate a typical example. All characteristics, functions, and computed results were compared and analyzed. Results Ten types of software were included, including programming and non‐programming software. They were developed mainly based on Bayesian or frequentist theory. Most types of software have the characteristics of easy operation, easy mastery, exact calculation, or excellent graphing. However, there was no single software that performed accurate calculations with superior graphing; this could only be achieved through the combination of two or more types of software. Conclusion This study suggests that the user should choose the appropriate software according to personal programming basis, operational habits, and financial ability. Then, the choice of the combination of BUGS and R (or Stata) software to perform the NMA is considered.