Kuss Oliver, Hoyer Annika, Solms Alexander
Institute of Medical Epidemiology, Biostatistics, and Informatics, University of Halle-Wittenberg, Halle (Saale), Germany.
Stat Med. 2014 Jan 15;33(1):17-30. doi: 10.1002/sim.5909. Epub 2013 Jul 19.
There are still challenges when meta-analyzing data from studies on diagnostic accuracy. This is mainly due to the bivariate nature of the response where information on sensitivity and specificity must be summarized while accounting for their correlation within a single trial. In this paper, we propose a new statistical model for the meta-analysis for diagnostic accuracy studies. This model uses beta-binomial distributions for the marginal numbers of true positives and true negatives and links these margins by a bivariate copula distribution. The new model comes with all the features of the current standard model, a bivariate logistic regression model with random effects, but has the additional advantages of a closed likelihood function and a larger flexibility for the correlation structure of sensitivity and specificity. In a simulation study, which compares three copula models and two implementations of the standard model, the Plackett and the Gauss copula do rarely perform worse but frequently better than the standard model. We use an example from a meta-analysis to judge the diagnostic accuracy of telomerase (a urinary tumor marker) for the diagnosis of primary bladder cancer for illustration.
在对诊断准确性研究的数据进行荟萃分析时，仍然存在挑战。这主要是由于反应的二元性质，即在单个试验中考虑敏感性和特异性信息的相关性时，必须对其进行汇总。在本文中，我们提出了一种用于诊断准确性研究荟萃分析的新统计模型。该模型对真阳性和真阴性的边际数使用贝塔-二项分布，并通过二元copula分布将这些边际联系起来。新模型具有当前标准模型（具有随机效应的二元逻辑回归模型）的所有特征，但具有封闭似然函数以及对敏感性和特异性相关结构具有更大灵活性的额外优势。在一项模拟研究中，该研究比较了三种copula模型和标准模型的两种实现方式，Plackett和高斯copula很少表现得比标准模型差，但经常比标准模型表现更好。我们用一项荟萃分析中的例子来说明端粒酶（一种尿液肿瘤标志物）对原发性膀胱癌诊断的诊断准确性。
