The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Pairwise pseudolikelihood approach for adjusting selection bias in meta-analysis

메타분석의 선택 편향 보정을 위한 쌍별 유사가능도 접근법

  • Kuk, Sunghee (Department of Statistics, Inha University) ;
  • Lee, Woojoo (Department of Public Health Science, Graduate School of Public Health Seoul National University)
  • Received : 2020.06.03
  • Accepted : 2020.06.26
  • Published : 2020.08.31
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Abstract

Meta-analysis provides a way of integrating several independent studies of interest. Since small studies with statistically significant results are more likely to be published, publication bias, which is a special case of selection bias, often occurs in meta analysis. Conditional likelihood and weighted estimating equation have been proposed to deal with publication bias, but they require to specify a correct selection probability model. In contrast, the pairwise pseudolikelihood approach can correct publication bias without fully specifying the correct selection probability model, but its performance in meta-analysis was not investigated. In this paper, we perform a numerical study about whether the pairwise pseudolikelihood approach is effective for solving publication bias arising from typical meta-analysis settings.

메타 분석은 여러 연구 결과들을 종합시켜주는 분석 방법 중 하나이다. 이 때 수집되는 연구 문헌들은 소규모 연구인 경우 통계적으로 유의한 결과를 보이는 연구가 출간될 확률이 높기 때문에, 선택 편향의 특수한 경우인 출판 편향이 종종 발생한다. 선택 편향을 보정하는 방법에는 조건부 가능도와 가중 추정 방정식이 있는데 이 방법들은 실제 얻기 힘든 정확한 선택 확률 모형을 필요로한다. 반면 쌍별 유사가능도 접근법은 선택 확률 모형을 정확히 알 수 없는 경우에도 선택 편향을 보정할 수 있는 방법으로 제안되었다. 본 논문은 메타분석에서 쌍별 유사가능도 접근법의 성능과 문제점을 수치적으로 연구한다.

Keywords

References

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