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- Network meta-analysis: application and practice using R software
- Sung Ryul Shim, Seong-Jang Kim, Jonghoo Lee, Gerta Rücker
- Epidemiol Health. 2019;41:e2019013. Published online April 8, 2019
- DOI: https://doi.org/10.4178/epih.e2019013
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- 224 Web of Science
- 186 Crossref
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Abstract
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Supplementary Material -
AbstractThe objective of this study is to describe the general approaches to network meta-analysis that are available for quantitative data synthesis using R software. We conducted a network meta-analysis using two approaches: Bayesian and frequentist methods. The corresponding R packages were “gemtc” for the Bayesian approach and “netmeta” for the frequentist approach. In estimating a network meta-analysis model using a Bayesian framework, the “rjags” package is a common tool. “rjags” implements Markov chain Monte Carlo simulation with a graphical output. The estimated overall effect sizes, test for heterogeneity, moderator effects, and publication bias were reported using R software. The authors focus on two flexible models, Bayesian and frequentist, to determine overall effect sizes in network meta-analysis. This study focused on the practical methods of network meta-analysis rather than theoretical concepts, making the material easy to understand for Korean researchers who did not major in statistics. The authors hope that this study will help many Korean researchers to perform network meta-analyses and conduct related research more easily with R software.
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SummaryKorean summary
통계프로그램으로서의 R이 대세인 연구환경에서 R을 활용한 체계적인 메타분석 안내서가 요구되어지며 본 연구는 이에 대한 좋은 해답이 될 것이다. 더욱이 본 연구는 네트워크 메타분석 (Network meta-analysis)의 이론과 더불어 R을 활용한 실습위주의 내용으로 구성되어져 있어 누구나 쉽게 실행할 수 있을 것이다. 본 연구를 통하여 많은 연구자들이 보다 쉽게 본인의 연구분야에 적용하여 관련 연구가 활성화되기를 바란다. -
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- Network meta-analysis: application and practice using Stata
- Sungryul Shim, Byung-Ho Yoon, In-Soo Shin, Jong-Myon Bae
- Epidemiol Health. 2017;39:e2017047. Published online October 27, 2017
- DOI: https://doi.org/10.4178/epih.e2017047
- 45,072 View
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- 322 Web of Science
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Abstract
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AbstractThis 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 synthesize evidences for a decision making by evaluating the comparative effectiveness of more than two alternative interventions for the same condition. Before conducting a NMA, 3 major assumptions—similarity, transitivity, and consistency—should be checked. The statistical analysis consists of 5 steps. The first step is to draw a network geometry to provide an overview of the network relationship. The second step checks the assumption of consistency. The third step is to make the network forest plot or interval plot in order to illustrate the summary size of comparative effectiveness among various interventions. The fourth step calculates cumulative rankings for identifying superiority among interventions. The last step evaluates 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 healthcare system.
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SummaryKorean summary
같은 치료 목적을 가진 여러 치료법들 간의 효과를 상호 비교한 결과들을 통합하기 위해 개발된 네트워크 메타분석 (NMA) 결과는 의사결정의 우선순위를 정하는데 있어 가장 상위의 과학적 설득력을 갖는다. STATA 통계프로그램에서 제공하는 빈도주의 틀에 기반한 NMA를 수행하는데 점검해야 할 가정들과 실제 적용 과정을 소개한다. -
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Clinical Epidemiology.2023; Volume 15: 1041. CrossRef - Evidence-based health: mathematical strategies for translating scientific findings into routine clinical care
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Neuromodulation: Technology at the Neural Interface.2022; 25(8): 1197. CrossRef - Maintenance Therapy for Preventing Endometrioma Recurrence after Endometriosis Resection Surgery – A Systematic Review and Network Meta-analysis
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Thierry Facon, Jesús San-Miguel, Meletios A. Dimopoulos, Maria-Victoria Mateos, Michele Cavo, Sophie van Beekhuizen, Zijiao Yuan, João Mendes, Annette Lam, Jianming He, Eric Ammann, Shaji Kumar
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Frontiers in Pharmacology.2022;[Epub] CrossRef - Bevacizumab versus PARP-inhibitors in women with newly diagnosed ovarian cancer: a network meta-analysis
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