Improved confidence regions in meta-analysis of diagnostic test accuracy

Tsubasa Ito, Shonosuke Sugasawa

研究成果: Article査読


Meta-analyses of diagnostic test accuracy (DTA) studies have been gathering attention in research in clinical epidemiology and health technology development, and bivariate random-effects model is becoming a standard tool. However, standard inference methods usually underestimate statistical errors and possibly provide highly overconfident results under realistic situations since they ignore the variability in the estimation of variance parameters. To overcome the difficulty, a new improved inference method, namely, an accurate confidence region for the meta-analysis of DTA, by asymptotically expanding the coverage probability of the standard confidence region. The advantage of the proposed confidence region is that it holds a relatively simple expression and does not require any repeated calculations such as Bootstrap or Monte Carlo methods to compute the region, thereby the proposed method can be easily carried out in practical applications. The effectiveness of the proposed method is demonstrated through simulation studies and an application to meta-analysis of screening test accuracy for alcohol problems.

ジャーナルComputational Statistics and Data Analysis
出版ステータスPublished - 2021 1月

ASJC Scopus subject areas

  • 統計学および確率
  • 計算数学
  • 計算理論と計算数学
  • 応用数学


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