Latent variable modeling of diagnostic accuracy.
Latent variable modeling of diagnostic accuracy.
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DOI：
10.2307/2533555
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发表时间：
1997-09
期刊：
影响因子：
1.9
通讯作者：
I. Yang;M. Becker
中科院分区：
文献类型：
--
作者：
I. Yang;M. Becker
潜在类别分析已应用于医学研究，以评估诊断测试/诊断医生的灵敏度和特异性。在这些应用中，假设对应于未观察到的患者真实疾病状态的二分潜在变量。多个诊断测试之间的关联归因于潜在变量引起的未观察到的异质性，即使真实的疾病状态未知，也可以推断诊断测试的灵敏度和特异性。然而，这种方法的诊断测试分析的缺点是，诊断测试之间的条件独立性的标准假设给定一个潜在的类是禁忌的数据在某些应用程序中。在本文中，模型将诊断测试之间的依赖性给定一个潜在的类提出。该模型被参数化，使得诊断测试的灵敏度和特异性是模型参数的简单函数，并且通常的潜在类模型作为特例获得。边缘模型用于解释每个潜在类内的依赖关系。一个加速EM梯度算法证明，以获得感兴趣的参数的最大似然估计，以及估计的精度估计。
Latent class analysis has been applied in medical research to assessing the sensitivity and specificity of diagnostic tests/diagnosticians. In these applications, a dichotomous latent variable corresponding to the unobserved true disease status of the patients is assumed. Associations among multiple diagnostic tests are attributed to the unobserved heterogeneity induced by the latent variable, and inferences for the sensitivities and specificities of the diagnostic tests are made possible even though the true disease status is unknown. However, a shortcoming of this approach to analyses of diagnostic tests is that the standard assumption of conditional independence among the diagnostic tests given a latent class is contraindicated by the data in some applications. In the present paper, models incorporating dependence among the diagnostic tests given a latent class are proposed. The models are parameterized so that the sensitivities and specificities of the diagnostic tests are simple functions of model parameters, and the usual latent class model obtains as a special case. Marginal models are used to account for the dependencies within each latent class. An accelerated EM gradient algorithm is demonstrated to obtain maximum likelihood estimates of the parameters of interest, as well as estimates of the precision of the estimates.