As meta-analysis of multiple diagnostic tests impacts clinical decision making and patient health, there is an increasing body of research in models and methods for meta-analysis of studies comparing multiple diagnostic tests. The application of the existing models to compare the accuracy of three or more tests suffers from the curse of multi-dimensionality, that is, either the number of model parameters increases rapidly or high dimensional integration is required. To overcome these issues in joint meta-analysis of studies comparing T > 2 diagnostic tests in a multiple tests design with a gold standard, we propose a model that assumes the true positives and true negatives for each test are conditionally independent and binomially distributed given the 2T-variate latent vector of sensitivities and specificities. For the random effects distribution, we employ a one-factor copula that provides tail dependence or tail asymmetry. Maximum likelihood estimation of the model is straightforward as the derivation of the likelihood requires bi-dimensional instead of 2T-dimensional integration. Our methodology is demonstrated with an extensive simulation study and an application example that determines which is the best test for the diagnosis of rheumatoid arthritis.

中文翻译：

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用于多种诊断测试联合荟萃分析的单因素 copula 混合模型

由于多项诊断测试的荟萃分析会影响临床决策和患者健康，因此越来越多的模型和方法研究对比较多项诊断测试的研究进行荟萃分析。应用现有模型比较三个或更多测试的准确性存在多维灾难，即模型参数数量快速增加或需要高维集成。为了克服在多个测试设计中将T > 2 诊断测试与金标准进行比较的研究的联合荟萃分析中的这些问题 ，我们提出了一个模型，该模型假设每个测试的真阳性和真阴性是条件独立且二项式分布的2吨-改变敏感性和特异性的潜在向量。对于随机效应分布，我们采用提供尾依赖或尾不对称的单因素 copula。模型的最大似然估计很简单，因为似然的推导需要二维而不是 2 T维积分。我们的方法通过广泛的模拟研究和一个应用示例进行了演示，该示例确定了哪个是诊断类风湿性关节炎的最佳测试。