  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.
