Meta‐analyses of diagnostic test accuracy (DTA) studies have been gaining prominence in research in clinical epidemiology and health technology development. In these DTA meta‐analyses, some studies may have markedly different characteristics from the others and potentially be inappropriate to include. The inclusion of these “outlying” studies might lead to biases, yielding misleading results. In addition, there might be influential studies that have notable impacts on the results. In this article, we propose Bayesian methods for detecting outlying studies and their influence diagnostics in DTA meta‐analyses. Synthetic influence measures based on the bivariate hierarchical Bayesian random effects models are developed because the overall influences of individual studies should be simultaneously assessed by the two outcome variables and their correlation information. We propose four synthetic measures for influence analyses: (a) relative distance, (b) standardized residual, (c) Bayesian p‐value, and (d) influence statistic on the area under the summary receiver operating characteristic curve. We also show that conventional univariate Bayesian influential measures can be applied to the bivariate random effects models, which can be used as marginal influential measures. Most of these methods can be similarly applied to the frequentist framework. We illustrate the effectiveness of the proposed methods by applying them to a DTA meta‐analysis of ultrasound in screening for vesicoureteral reflux among children with urinary tract infections.

中文翻译：

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影响诊断和离群值检测，以对诊断测试的准确性进行荟萃分析。

诊断检验准确度（DTA）研究的荟萃分析在临床流行病学和卫生技术发展的研究中日益受到重视。在这些DTA荟萃分析中，某些研究可能与其他研究具有明显不同的特征，因此可能不宜将其包括在内。纳入这些“外围”研究可能会导致偏差，从而产生误导性的结果。此外，可能会有影响力的研究对结果产生显着影响。在本文中，我们提出了用于在DTA荟萃分析中检测偏远研究及其影响诊断的贝叶斯方法。由于基于两个结果变量及其相关信息应同时评估单个研究的总体影响，因此开发了基于二元分层贝叶斯随机效应模型的综合影响量度。我们提出了四种综合措施来进行影响分析：（a）相对距离，（b）标准化残差，（c）贝叶斯p值和（d）汇总接收器工作特性曲线下面积的影响统计量。我们还表明，常规的单变量贝叶斯影响度量可以应用于双变量随机效应模型，该模型可以用作边际影响度量。这些方法中的大多数都可以类似地应用于常客框架。