论文标题
多种诊断测试的联合荟萃分析的多项式截短的D-Vine Copula混合模型
A multinomial truncated D-vine copula mixed model for the joint meta-analysis of multiple diagnostic tests
论文作者
论文摘要
关于诊断研究的荟萃分析的方法有广泛的文献，但它主要集中于单个测试。但是，对特定疾病的更好理解导致了多个测试的发展。最近提出了一种多项式通用线性混合模型（GLMM），用于比较多个测试的研究的关节荟萃分析。我们提出了一个新的模型，用于多次测试的联合荟萃分析，该模型假设每种测试组合的计数独立的多项式分布会导致患病和未病患者的患者的次数，这是基于每种测试组合的潜在概率导致疾病和未诊断患者的概率。对于潜在比例的随机效应分布，我们采用了可覆盖柔性依赖性结构的可截断的可绘制的藤腹co。提出的模型包括多项式GLMM作为特殊情况，但也可以以潜在比例的原始规模运行。通过模拟研究和对唐氏综合症筛查的荟萃分析进行了两种测试：缩短肱骨和缩短股骨。我们的方法与多项式GLMM的比较在改变当前结论的实际数据荟萃分析中得出了发现。
There is an extensive literature on methods for meta-analysis of diagnostic studies, but it mainly focuses on a single test. However, the better understanding of a particular disease has led to the development of multiple tests. A multinomial generalized linear mixed model (GLMM) is recently proposed for the joint meta-analysis of studies comparing multiple tests. We propose a novel model for the joint meta-analysis of multiple tests, which assumes independent multinomial distributions for the counts of each combination of test results in diseased and non-diseased patients, conditional on the latent vector of probabilities of each combination of test results in diseased and non-diseased patients. For the random effects distribution of the latent proportions, we employ a truncated drawable vine copula that can cover flexible dependence structures. The proposed model includes the multinomial GLMM as a special case, but can also operate on the original scale of the latent proportions. Our methodology is demonstrated with a simulation study and using a meta-analysis of screening for Down syndrome with two tests: shortened humerus and shortened femur. The comparison of our method with the multinomial GLMM yields findings in the real data meta-analysis that change the current conclusions.
