In diagnostic test accuracy meta-analysis (DTA-MA), standard inference methods using bivariate random-effects models for jointly synthesizing sensitivity and specificity can be sensitive to outlying studies and may yield misleading conclusions. In this article, we propose frequentist outlier-robust statistical inference methods for DTA-MA based on density power divergence. The proposed methods automatically downweight influential outlying studies by modifying the estimating function using the robust divergence with a tuning parameter. To achieve robust yet statistically efficient inference in the presence of outlying studies, the proposed methods incorporate practical strategies for selecting the tuning parameter, including a data-adaptive criterion based on the Hyvärinen score. We also quantify the contributions of individual studies to the robust pooled estimates, facilitating interpretation of how outlying studies affect the results. We illustrate the effectiveness of the proposed methods through an application to a DTA-MA of the Mini-Mental State Examination. Simulation studies showed that the proposed methods reduced bias and root mean squared error relative to existing methods and improved coverage probability in the presence of outliers. The proposed methods enable a sensitivity analysis to assess whether the main results obtained using standard methods are driven by outlying studies.

翻译：在诊断测试准确性荟萃分析（DTA-MA）中，采用双变量随机效应模型联合综合敏感性和特异性的标准推断方法容易受到异常值研究的影响，并可能导致误导性结论。本文基于密度功率散度提出针对DTA-MA的频率学派稳健统计推断方法。该方法通过使用带有调节参数的稳健散度修改估计函数，自动降低影响性异常值研究的权重。为在存在异常值研究时实现既稳健又统计高效的推断，所提方法融入了选择调节参数的实用策略，包括基于Hyvärinen评分的数据自适应准则。我们还量化了单个研究对稳健合并估计的贡献，有助于解释异常值研究如何影响结果。通过将方法应用于简易精神状态检查的DTA-MA实例，验证了其有效性。模拟研究表明，与现有方法相比，所提方法在存在异常值时减少了偏差和均方根误差，并提高了覆盖概率。所提方法能够进行敏感性分析，以评估使用标准方法获得的主要结果是否由异常值研究驱动。