Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
권호 표지

Robust Bayesian meta analysis

로버스트 베이지안 메타분석

  • Choi, Seong-Mi (Department of Statistics, Kyungpook National University) ;
  • Kim, Dal-Ho (Department of Statistics, Kyungpook National University) ;
  • Shin, Im-Hee (Department of Medical Statistics, School of Medicine, Catholic University of Daegu) ;
  • Kim, Ho-Gak (Division of Gastroenterology, Department of Internal Medicine, School of Medicine, Catholic University of Daegu) ;
  • Kim, Sang-Gyung (Department of Laboratory Medicine, School of Medicine, Catholic University of Daegu)
  • 최성미 (경북대학교 통계학과) ;
  • 김달호 (경북대학교 통계학과) ;
  • 신임희 (대구가톨릭대학교 의과대학 의학통계학과) ;
  • 김호각 (대구가톨릭대학교 의과대학 소화기내과) ;
  • 김상경 (대구가톨릭대학교 의과대학 진단검사의학과)
  • Received : 2011.04.28
  • Accepted : 2011.05.22
  • Published : 2011.05.31
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Abstract

This article addresses robust Bayesian modeling for meta analysis which derives general conclusion by combining independently performed individual studies. Specifically, we propose hierarchical Bayesian models with unknown variances for meta analysis under priors which are scale mixtures of normal, and thus have tail heavier than that of the normal. For the numerical analysis, we use the Gibbs sampler for calculating Bayesian estimators and illustrate the proposed methods using actual data.

본 논문은 독립적으로 수행된 연구결과를 합쳐서 일반적인 결론을 도출하는 메타분석을 위한 로버스트 계층적 베이지안 모형을 고려한다. 사전정보가 정규분포를 따른다는 가정 대신 정규분포의 척도혼합을 사용하여 정규분포보다 더 두꺼운 꼬리를 가지는 사전분포를 사용한다. 나아가 개별 분석의 분산이 알려져 있지 않은 경우를 계층적 베이지안 모형에 포함하여 메타분석을 수행하고자 한다. 깁스 표집을 사용하여 추정값을 계산하고, 실제 자료를 사용하여 제안된 방법을 예시한다.

Keywords

References

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