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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
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AbstractAbstract AbstractSummary PDFSupplementary Material
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 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.
Summary
Korean 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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Meta-analysis for genome-wide association studies using case-control design: application and practice
Sungryul Shim, Jiyoung Kim, Wonguen Jung, In-Soo Shin, Jong-Myon Bae
Epidemiol Health. 2016;38:e2016058.   Published online December 18, 2016
DOI: https://doi.org/10.4178/epih.e2016058
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AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
This review aimed to arrange the process of a systematic review of genome-wide association studies in order to practice and apply a genome-wide meta-analysis (GWMA). The process has a series of five steps: searching and selection, extraction of related information, evaluation of validity, meta-analysis by type of genetic model, and evaluation of heterogeneity. In contrast to intervention meta-analyses, GWMA has to evaluate the Hardy–Weinberg equilibrium (HWE) in the third step and conduct meta-analyses by five potential genetic models, including dominant, recessive, homozygote contrast, heterozygote contrast, and allelic contrast in the fourth step. The ‘genhwcci’ and ‘metan’ commands of STATA software evaluate the HWE and calculate a summary effect size, respectively. A meta-regression using the ‘metareg’ command of STATA should be conducted to evaluate related factors of heterogeneities.
Summary
Korean summary
오늘날 활발히 수행되고 있는 유전체역학연구는 재현성의 문제, 대상자 크기의 한계 등으로 유전체 메타분석 연구가 요구되고 있다. 유전체 메타분석의 전반적인 수행 단계는 약물, 수술 등의 중개연구의 메타분석처럼 5 단계를 밟아 수행된다. 그러나, 유전체 메타분석은 3번째 과정에서 Hardy–Weinberg equilibrium (HWE) 검정과 4번째 과정에서 5가지 가능한 유전형 모델에 따라 메타분석이 이루어진다는 점에서 중개연구의 메타분석과 차이가 있다.

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