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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Published in	PloS one Vol. 8; no. 10; p. e76654
Main Authors	Chaimani, Anna, Higgins, Julian P. T., Mavridis, Dimitris, Spyridonos, Panagiota, Salanti, Georgia
Format	Journal Article
Language	English
Published	United States Public Library of Science 03.10.2013 Public Library of Science (PLoS)
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 Greece United Kingdom > UK
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Abstract	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.
AbstractList	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. 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.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.
Audience	Academic
Author	Higgins, Julian P. T. Salanti, Georgia Mavridis, Dimitris Chaimani, Anna Spyridonos, Panagiota
AuthorAffiliation	4 Department of Primary Education, University of Ioannina, Ioannina, Greece 5 Department of Medical Physics, School of Medicine, University of Ioannina, Ioannina, Greece 1 Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece Institut de recherches cliniques de Montréal (IRCM), Canada 3 Centre for Reviews and Dissemination, University of York, York, United Kingdom 2 School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
AuthorAffiliation_xml	– name: 4 Department of Primary Education, University of Ioannina, Ioannina, Greece – name: 5 Department of Medical Physics, School of Medicine, University of Ioannina, Ioannina, Greece – name: 3 Centre for Reviews and Dissemination, University of York, York, United Kingdom – name: 2 School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom – name: 1 Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece – name: Institut de recherches cliniques de Montréal (IRCM), Canada
Author_xml	– sequence: 1 givenname: Anna surname: Chaimani fullname: Chaimani, Anna – sequence: 2 givenname: Julian P. T. surname: Higgins fullname: Higgins, Julian P. T. – sequence: 3 givenname: Dimitris surname: Mavridis fullname: Mavridis, Dimitris – sequence: 4 givenname: Panagiota surname: Spyridonos fullname: Spyridonos, Panagiota – sequence: 5 givenname: Georgia surname: Salanti fullname: Salanti, Georgia
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/24098547$$D View this record in MEDLINE/PubMed
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Notes	ObjectType-Article-2 SourceType-Scholarly Journals-1 content type line 14 ObjectType-Feature-3 ObjectType-Evidence Based Healthcare-1 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 Conceived and designed the experiments: AC JPTH GS. Performed the experiments: AC DM PS. Analyzed the data: AC DM PS. Contributed reagents/materials/analysis tools: AC JPTH DM GS. Wrote the paper: AC JPTH DM PS GS. Competing Interests: The authors have declared that no competing interests exist.
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SubjectTerms	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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Title	Graphical Tools for Network Meta-Analysis in STATA
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