论文标题
一种用于多个诊断测试联合荟萃分析的单因素副群混合模型
An one-factor copula mixed model for joint meta-analysis of multiple diagnostic tests
论文作者
论文摘要
由于对多项诊断测试的荟萃分析会影响临床决策和患者健康，因此在模型和方法中，研究的研究越来越多，用于比较多个诊断测试的研究的荟萃分析。现有模型的应用比较了三个或多个测试的准确性，遭受了多维性的诅咒，即需要迅速增加模型参数的数量或需要高维积分。为了克服这些问题的研究，在多个测试设计中比较$ t> 2 $诊断测试的研究与黄金标准进行了比较，我们提出了一个模型，该模型假设每个测试的真实阳性和真正的负面因素有条件地独立且二元分布，鉴于$ 2T $ 2T $ 2t $ - 变量的敏感性和特殊性。对于随机效应分布，我们采用一种单因素副群，可提供尾部依赖性或尾部不对称性。模型的最大似然估计是直接的，因为可能性的推导需要双维而不是$ 2T $维的集成。通过广泛的模拟研究和一个应用示例来证明我们的方法是确定哪种方法是诊断类风湿关节炎的最佳测试。
As the meta-analysis of more than one diagnostic tests can impact 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, i.e., either the number of model parameters increase 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 an 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.
