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.

中文翻译：

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改进了诊断测试准确性荟萃分析的置信区域

摘要 诊断测试准确性 (DTA) 研究的 Meta 分析在临床流行病学和卫生技术发展的研究中备受关注，双变量随机效应模型正在成为标准工具。然而，标准推理方法通常会低估统计误差，并可能在现实情况下提供高度过度自信的结果，因为它们忽略了方差参数估计中的可变性。为了克服这个困难，一种新的改进的推理方法，即DTA元分析的准确置信区域，通过渐近扩展标准置信区域的覆盖概率。所提出的置信区域的优点是它的表达式相对简单，不需要任何重复计算，如Bootstrap或Monte Carlo方法来计算该区域，因此所提出的方法可以很容易地在实际应用中进行。通过模拟研究和应用于酒精问题筛查测试准确性的荟萃分析，证明了所提出方法的有效性。