Nikoloulopoulos Aristidis K
School of Computing Sciences, University of East Anglia, Norwich NR4 7TJ, U.K.
Stat Med. 2015 Dec 20;34(29):3842-65. doi: 10.1002/sim.6595. Epub 2015 Aug 2.
Diagnostic test accuracy studies typically report the number of true positives, false positives, true negatives and false negatives. There usually exists a negative association between the number of true positives and true negatives, because studies that adopt less stringent criterion for declaring a test positive invoke higher sensitivities and lower specificities. A generalized linear mixed model (GLMM) is currently recommended to synthesize diagnostic test accuracy studies. We propose a copula mixed model for bivariate meta-analysis of diagnostic test accuracy studies. Our general model includes the GLMM as a special case and can also operate on the original scale of sensitivity and specificity. Summary receiver operating characteristic curves are deduced for the proposed model through quantile regression techniques and different characterizations of the bivariate random effects distribution. Our general methodology is demonstrated with an extensive simulation study and illustrated by re-analysing the data of two published meta-analyses. Our study suggests that there can be an improvement on GLMM in fit to data and makes the argument for moving to copula random effects models. Our modelling framework is implemented in the package CopulaREMADA within the open source statistical environment R.
诊断试验准确性研究通常会报告真阳性、假阳性、真阴性和假阴性的数量。真阳性和真阴性的数量之间通常存在负相关关系，因为采用不太严格的标准来判定试验为阳性的研究具有较高的敏感性和较低的特异性。目前推荐使用广义线性混合模型（GLMM）来综合诊断试验准确性研究。我们提出了一种用于诊断试验准确性研究双变量荟萃分析的copula混合模型。我们的通用模型将GLMM作为一个特殊情况包含在内，并且还可以在敏感性和特异性的原始尺度上运行。通过分位数回归技术和双变量随机效应分布的不同特征，推导出了所提模型的汇总受试者工作特征曲线。我们通过广泛的模拟研究展示了我们的通用方法，并通过重新分析两项已发表的荟萃分析的数据进行了说明。我们的研究表明，在数据拟合方面，copula随机效应模型相较于GLMM可能会有所改进，并支持转向copula随机效应模型。我们的建模框架在开源统计环境R中的CopulaREMADA包中得以实现。
