Literature DB >> 34550594

Software to Conduct a Meta-Analysis and Network Meta-Analysis.

Caitlin Daly1,2, Charlene Soobiah3,4.   

Abstract

Statistical software for meta-analysis (MA) and network meta-analysis (NMA) have become indispensable for researchers. The aim of this chapter is to introduce key features of MA and NMA software to compare the effectiveness of interventions. Commonly used or routinely maintained statistical software are reviewed, including commercial and open-sourced programs such as Stata, R and Excel plug-ins. It does not provide a comprehensive overview of all features available in the software covered. Rather, it focuses on the essential features required to carry out an MA or NMA . This chapter begins with a review of key considerations when implementing an MA or NMA , then presents a summary of the software. Key features of each software option are discussed.
© 2022. Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  BUGS; Meta-analysis; MetaInsight; MetaXL; NetMetaXL; Network meta-analysis; R; Review Manager; SAS; Stata

Mesh：

Year:  2022        PMID： 34550594     DOI： 10.1007/978-1-0716-1566-9_14

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN： 1064-3745


  34 in total

Review 1.  Bayesian methods in meta-analysis and evidence synthesis.

Authors:  A J Sutton; K R Abrams
Journal:  Stat Methods Med Res       Date:  2001-08       Impact factor: 3.021

2.  Statistical aspects of the analysis of data from retrospective studies of disease.

Authors:  N MANTEL; W HAENSZEL
Journal:  J Natl Cancer Inst       Date:  1959-04       Impact factor: 13.506

3.  What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data.

Authors:  Michael J Sweeting; Alexander J Sutton; Paul C Lambert
Journal:  Stat Med       Date:  2004-05-15       Impact factor: 2.373

4.  Why add anything to nothing? The arcsine difference as a measure of treatment effect in meta-analysis with zero cells.

Authors:  Gerta Rücker; Guido Schwarzer; James Carpenter; Ingram Olkin
Journal:  Stat Med       Date:  2009-02-28       Impact factor: 2.373

5.  Beta blockade during and after myocardial infarction: an overview of the randomized trials.

Authors:  S Yusuf; R Peto; J Lewis; R Collins; P Sleight
Journal:  Prog Cardiovasc Dis       Date:  1985 Mar-Apr       Impact factor: 8.194

6.  A general framework for the use of logistic regression models in meta-analysis.

Authors:  Mark C Simmonds; Julian Pt Higgins
Journal:  Stat Methods Med Res       Date:  2014-05-12       Impact factor: 3.021

Review 7.  Network meta-analysis using R: a review of currently available automated packages.

Authors:  Binod Neupane; Danielle Richer; Ashley Joel Bonner; Taddele Kibret; Joseph Beyene
Journal:  PLoS One       Date:  2014-12-26       Impact factor: 3.240

Review 8.  When should meta-analysis avoid making hidden normality assumptions?

Authors:  Dan Jackson; Ian R White
Journal:  Biom J       Date:  2018-07-30       Impact factor: 2.207

9.  BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network Meta-analyses.

Authors:  Audrey Béliveau; Devon J Boyne; Justin Slater; Darren Brenner; Paul Arora
Journal:  BMC Med Res Methodol       Date:  2019-10-22       Impact factor: 4.615

10.  A systematic comparison of software dedicated to meta-analysis of causal studies.

Authors:  Leon Bax; Ly-Mee Yu; Noriaki Ikeda; Karel G M Moons
Journal:  BMC Med Res Methodol       Date:  2007-09-10       Impact factor: 4.615
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