Improved confidence regions in meta-analysis of diagnostic test accuracy

Tsubasa Ito, Shonosuke Sugasawa

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article number107068
JournalComputational Statistics and Data Analysis
Volume153
DOIs
Publication statusPublished - 2021 Jan
Externally publishedYes

Keywords

  • Asymptotic expansion
  • Bias correction
  • Confidence region
  • Random-effects model

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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