Nonparametric analysis for the ROC areas of two diagnostic tests in the presence of nonignorable verification bias
Nonparametric analysis for the ROC areas of two diagnostic tests in the presence of nonignorable verification bias
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DOI：
10.1016/s0378-3758(02)00146-5
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发表时间：
2003-07-01
影响因子：
0.9
通讯作者：
Castelluccio, P
中科院分区：
文献类型：
--
作者：
Zhou, XH;Castelluccio, P
配对设计经常被用来评估竞争性诊断测试的准确性。配对设计中的一个常见问题是验证偏差，当一些患者没有接受疾病验证，并且验证的原因取决于测试结果和其他因素时，就会发生验证偏差。现有的验证偏差校正方法大多假设验证过程是可忽略的，这意味着验证患者的概率只取决于观察到的协变量，而不取决于患者未观察到的疾病状态。据我们所知，这是第一次将不可忽视的验证与中华民国面积估算相结合的尝试。争论的焦点是极大似然(ML)方程可能存在多个局部极大值和边界解，这会使极大似然估计的计算复杂化。为了解决这一计算困难，我们提出了一种Profile方法和EM算法相结合的方法来寻找所提出的不可忽略验证模型下感兴趣参数的全局ML估计。此外，我们还找到了边界点问题的一个简单解。在获得最大似然估计后，我们对两个诊断测试的相对准确性提出了基于似然的推断。分析中的另一个问题是所提出的不可忽略的验证机制模型的拟合优度。我们建议对所提出的不可忽略模型使用基于Bootstrap的拟合优度检验。最后，我们将我们的方法应用于一项实际研究的数据，这项研究推动了这项研究。在这个例子中，我们分析的目的是比较MRI和CT成像在检测晚期胰腺癌方面的相对准确性。(C)2002 Elsevier Science B.V.保留所有权利。
A paired design is often used to evaluate the accuracies of competing diagnostic tests. One common problem in a paired design is verification bias which occurs when some patients do not receive disease verification and when the reason for verification depends on the test results and other factors. Most of the existing methods for verification bias correction assume the verification process is ignorable, which means that the probability of verifying a patient depends only on the observed covariates, but not on the unobserved disease status of the patient.In this paper, we develop a bias correction procedure without assuming ignorability of the verification mechanism. As far as we know, this proposed method is the first attempt to combine nonignorable verification with estimation of the ROC areas. At issue is the possible existence of multiple local maxima and boundary solutions for the maximum likelihood (ML) equations, which can complicate the computation of the ML estimates. To deal with this computational difficulty, we propose a profile method combined with the EM algorithm to find the global ML estimators for the parameters of interest under a proposed nonignorable verification model. Furthermore, we also find a simple solution to the boundary point problem. After obtaining the ML estimates, we propose likelihood-based inferences on the relative accuracy of two diagnostic tests. Another issue in the analysis is the goodness-of-fit of the proposed nonignorable model for the verification mechanism. We propose to use a bootstrap-based goodness-of-fit test for the proposed nonignorable model. Finally, we apply our method to data from a real study that has motivated this research. In this example, the aim of our analysis is to compare the relative accuracy of MRI and CT imaging in detecting advanced stage pancreatic cancer. (C) 2002 Elsevier Science B.V. All rights reserved.