Graphical tools for network meta-analysis in STATA

Network meta-analysis synthesizes direct and indirect evidence in a network of trials that compare multiple interventions and has the potential to rank the competing treatments according to the studied outcome. Despite its usefulness network meta-analysis is often criticized for its complexity and f...

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Bibliographic Details
Published in:PloS one 2013-10, Vol.8 (10), p.e76654-e76654
Main Authors: Chaimani, Anna, Higgins, Julian P T, Mavridis, Dimitris, Spyridonos, Panagiota, Salanti, Georgia
Format: Article
Language:English
Subjects:
Accessibility
Acute Disease
Antimanic Agents - therapeutic use
Antirheumatic Agents - therapeutic use
Arthritis, Rheumatoid - drug therapy
Arthritis, Rheumatoid - physiopathology
Bias
Bipolar Disorder - drug therapy
Bipolar Disorder - physiopathology
Cardiovascular disease
Clinical trials
Computer applications
Data Interpretation, Statistical
Decision making
Epidemiology
Humans
Hygiene
Mathematical models
Medicine
Meta-analysis
Meta-Analysis as Topic
Models, Statistical
Odds Ratio
Research Design
Researchers
Studies
Treatment Outcome
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Summary:Network meta-analysis synthesizes direct and indirect evidence in a network of trials that compare multiple interventions and has the potential to rank the competing treatments according to the studied outcome. Despite its usefulness network meta-analysis is often criticized for its complexity and for being accessible only to researchers with strong statistical and computational skills. The evaluation of the underlying model assumptions, the statistical technicalities and presentation of the results in a concise and understandable way are all challenging aspects in the network meta-analysis methodology. In this paper we aim to make the methodology accessible to non-statisticians by presenting and explaining a series of graphical tools via worked examples. To this end, we provide a set of STATA routines that can be easily employed to present the evidence base, evaluate the assumptions, fit the network meta-analysis model and interpret its results.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0076654