Meta-analysis of diagnostic tests accounting for disease prevalence: a new model using trivariate copulas
Meta-analysis of diagnostic tests accounting for disease prevalence: a new model using trivariate copulas
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DOI：
10.1002/sim.6463
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发表时间：
2015-05-20
影响因子：
2
通讯作者：
Kuss, O.
中科院分区：
文献类型：
--
作者：
Hoyer, A.;Kuss, O.
在现实生活中，与生物统计教科书知识有些相反，诊断测试的敏感性和特异性（而不仅仅是预测值）可能会随着疾病的潜在患病率而变化。在诊断研究的荟萃分析中，考虑到这一事实自然会导致具有随机研究效应的传统双变量逻辑回归模型的三变量扩展。在本文中，提出了一种新模型，使用三变量联结函数和β二项式边缘分布来衡量敏感性、特异性和患病率，作为双变量模型的扩展。使用两种不同的联结，即三变量高斯联结和基于二元 Plackett 联结的三变量藤联结。该模型具有封闭形式的可能性，因此可以使用标准软件（例如 SAS PROC NLMIXED）。模拟研究的结果表明，Copula 模型的性能至少与标准模型一样好，但往往比标准模型更好。通过两个例子来说明这些方法。版权所有 (c) 2015John Wiley & Sons, Ltd.
In real life and somewhat contrary to biostatistical textbook knowledge, sensitivity and specificity (and not only predictive values) of diagnostic tests can vary with the underlying prevalence of disease. In meta-analysis of diagnostic studies, accounting for this fact naturally leads to a trivariate expansion of the traditional bivariate logistic regression model with random study effects. In this paper, a new model is proposed using trivariate copulas and beta-binomial marginal distributions for sensitivity, specificity, and prevalence as an expansion of the bivariate model. Two different copulas are used, the trivariate Gaussian copula and a trivariate vine copula based on the bivariate Plackett copula. This model has a closed-form likelihood, so standard software (e.g., SAS PROC NLMIXED) can be used. The results of a simulation study have shown that the copula models perform at least as good but frequently better than the standard model. The methods are illustrated by two examples. Copyright (c) 2015John Wiley & Sons, Ltd.