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Korean Journal of Epidemiology 2017;e2017047.
DOI: https://doi.org/10.4178/epih.e2017047    [Accepted] Published online October 27, 2017.
Network Meta-analysis: Application and Practice using STATA software
Sung-Ryul Shim1  , Byung-Ho Yoon2  , In-Soo Shin3  , Jong-Myon Bae4 
1Insitute for Clinical Molecular Biology Research, Soon Chun Hyang University Hospital, Seoul, Korea
2Department of Orthopaedic Surgery, Inje University College of Medicine, Seoul Paik Hospital, Seoul, Korea
3Department of Education, Jeonju University, Jeonju, Korea
4Jeju National University School of Medicine, Jeju, Korea
Correspondence  Jong-Myon Bae ,Tel: 064-755-5567, Fax: 064-725-2593, Email: jmbae@cheju.ac.kr
Submitted: September 6, 2017  Accepted after revision: October 27, 2017
Abstract
This review aimed to arrange the concepts of a network meta-analysis (NMA) and to demonstrate the analytical process of NMA using STATA software under frequentist framework. The NMA tries to synthesis evidences for a decision making with evaluating the comparative effectiveness of more than 2 alternative interventions for the same condition. Before conducting a NMA, three major assumptions such as similarity, transitivity, and consistency should be checked. The statistical analysis is processed of five steps. First step is to draw a network geometry in order to overview the network relationship. Second step is to check the assumption of consistency using STATA command of . Third step under the consistency is to make the network forest plot or interval plot in order to illustrate the summary size of comparative effectiveness among interventions. Fourth step is to calculate cumulative rankings for identifying a superiority among interventions. Last step is to evaluate publication bias or effect modifiers for a valid inference from results. The synthesized evidences through five steps would be very useful to evidence-based decision-making in healthcare. Thus NMA should be activated in order to guarantee the quality of health care system.
Keywords: Network meta-analysis; Multiple treatments meta-analysis; Mixed treatment comparison; Consistency; Transivity
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