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Software and package applicating for network meta-analysis: A usage-based comparative study.

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Summary
This summary is machine-generated.

This study compares network meta-analysis (NMA) software, finding no single tool excels at both calculation and graphing. Combining software like BUGS with R or Stata is recommended for optimal NMA results.

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Area of Science:

  • Biostatistics
  • Health Informatics
  • Pharmacometrics

Background:

  • Network meta-analysis (NMA) is a powerful statistical technique for synthesizing evidence from multiple studies.
  • Selecting appropriate software is crucial for conducting accurate and efficient NMA.

Purpose of the Study:

  • To compare and analyze the characteristics and functions of various software applications designed for network meta-analysis.
  • To guide researchers in choosing the most suitable software based on their needs and technical proficiency.

Main Methods:

  • A comprehensive literature search was conducted across major databases (PubMed, EMbase, Cochrane Library) and official websites.
  • Included software and packages published up to March 2016 were collected and evaluated.
  • A typical NMA example was computed using each software to compare characteristics, functions, and results.

Main Results:

  • Ten software types, including programming and non-programming options, were analyzed, primarily based on Bayesian or frequentist theories.
  • While most software offered ease of use, mastery, accurate calculations, or excellent graphing, no single application provided all.
  • Optimal NMA performance, combining accurate calculations with superior graphing, required integrating two or more software types.

Conclusions:

  • The choice of NMA software should align with the user's programming skills, operational preferences, and financial resources.
  • A combination of Bayesian inference Using Gibbs Sampling (BUGS) with R or Stata is a recommended approach for robust NMA.