# Improved Confidence Regions in Meta-analysis of Diagnostic Test Accuracy

## TL;DR

This paper introduces a new, simple, and accurate confidence region method for meta-analysis of diagnostic test accuracy, addressing the limitations of standard methods that underestimate errors.

## Contribution

The paper proposes an asymptotic expansion-based confidence region that improves inference accuracy without complex computations like bootstrap or Monte Carlo methods.

## Key findings

- The new method provides more accurate coverage probabilities.
- Simulation studies confirm its effectiveness over standard methods.
- Applied to alcohol screening test data, it yields more reliable confidence regions.

## 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.