Literature DB >> 22218368

Issues in performing a network meta-analysis.

Stephen Senn1, Francois Gavini, David Magrez, André Scheen.   

Abstract

The example of the analysis of a collection of trials in diabetes consisting of a sparsely connected network of 10 treatments is used to make some points about approaches to analysis. In particular various graphical and tabular presentations, both of the network and of the results are provided and the connection to the literature of incomplete blocks is made. It is clear from this example that is inappropriate to treat the main effect of trial as random and the implications of this for analysis are discussed. It is also argued that the generalisation from a classic random-effect meta-analysis to one applied to a network usually involves strong assumptions about the variance components involved. Despite this, it is concluded that such an analysis can be a useful way of exploring a set of trials.

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Year:  2012        PMID: 22218368     DOI: 10.1177/0962280211432220

Source DB:  PubMed          Journal:  Stat Methods Med Res        ISSN: 0962-2802            Impact factor:   3.021


  20 in total

Review 1.  Network meta-analysis: an introduction for pharmacists.

Authors:  Yina Xu; Mohamed Amine Amiche; Mina Tadrous
Journal:  Int J Clin Pharm       Date:  2018-10

2.  Detecting outlying trials in network meta-analysis.

Authors:  Jing Zhang; Haoda Fu; Bradley P Carlin
Journal:  Stat Med       Date:  2015-04-08       Impact factor: 2.373

Review 3.  The attractiveness of network meta-analysis: a comprehensive systematic and narrative review.

Authors:  Teresa Greco; Giuseppe Biondi-Zoccai; Omar Saleh; Laura Pasin; Luca Cabrini; Alberto Zangrillo; Giovanni Landoni
Journal:  Heart Lung Vessel       Date:  2015

4.  Network meta-analysis combining individual patient and aggregate data from a mixture of study designs with an application to pulmonary arterial hypertension.

Authors:  Howard H Z Thom; Gorana Capkun; Annamaria Cerulli; Richard M Nixon; Luke S Howard
Journal:  BMC Med Res Methodol       Date:  2015-04-12       Impact factor: 4.615

5.  Visualizing inconsistency in network meta-analysis by independent path decomposition.

Authors:  Ulrike Krahn; Harald Binder; Jochem König
Journal:  BMC Med Res Methodol       Date:  2014-12-16       Impact factor: 4.615

6.  Using structural equation modeling for network meta-analysis.

Authors:  Yu-Kang Tu; Yun-Chun Wu
Journal:  BMC Med Res Methodol       Date:  2017-07-14       Impact factor: 4.615

7.  A graphical tool for locating inconsistency in network meta-analyses.

Authors:  Ulrike Krahn; Harald Binder; Jochem König
Journal:  BMC Med Res Methodol       Date:  2013-03-09       Impact factor: 4.615

8.  Ranking treatments in frequentist network meta-analysis works without resampling methods.

Authors:  Gerta Rücker; Guido Schwarzer
Journal:  BMC Med Res Methodol       Date:  2015-07-31       Impact factor: 4.615

Review 9.  Comparative effectiveness of psychological treatments for depressive disorders in primary care: network meta-analysis.

Authors:  Klaus Linde; Gerta Rücker; Kirsten Sigterman; Susanne Jamil; Karin Meissner; Antonius Schneider; Levente Kriston
Journal:  BMC Fam Pract       Date:  2015-08-19       Impact factor: 2.497

10.  Network-meta analysis made easy: detection of inconsistency using factorial analysis-of-variance models.

Authors:  Hans-Peter Piepho
Journal:  BMC Med Res Methodol       Date:  2014-05-10       Impact factor: 4.615

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