Network meta-analysis: application and practice using R software

• 발행기관: 한국역학회
• 저자명: Sung Ryul Shim Seong-Jang Kim Jonghoo Lee Gerta Rü cker
• 간행물 정보: 『Epidemiology and Health』41, 1~10쪽, 전체 10쪽
• 주제분류: 의약학 > 면역학
• 파일형태: PDF
• 발행일자: 2019.01.01

영문 초록

The 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.

목차

INTRODUCTION

STATISTICAL APPROACH OF NETWORK META-ANALYSIS

BAYESIAN NMA USING R “gemtc” PACKAGE

FREQUENTIST NMA USING R “netmeta” PACKAGE

COMPARISON OF NMA RESULTS: BAYESIAN VS. FREQUENTIST METHOD AND R VS. STATA SOFTWARE

CONCLUSION

SUPPLEMENTARY MATERIALS

CONFLICT OF INTEREST

ACKNOWLEDGEMENTS

ORCID

REFERENCES

키워드

Network meta-analysis Multiple treatments meta-analysis Mixed treatment comparison Consistency Transitivity Bayes’ theorem