Sepúlveda R, Vicente-Villardón J L, Galindo M P
Department of Statistics, University of Salamanca, Salamanca, Spain.
Stat Med. 2008 May 20;27(11):1855-69. doi: 10.1002/sim.3194.
Latent class models (LCMs) can be used to assess diagnostic test performance when no reference test (a gold standard) is available, considering two latent classes representing disease or non-disease status. One of the basic assumptions in such models is that of local or conditional independence: all indicator variables (tests) are statistically independent within each latent class. However, in practice this assumption is often violated; hence, the two-LCM fits the data poorly. In this paper, we propose the use of Biplot methods to identify the conditional dependence between pairs of manifest variables within each latent class. Additionally, we propose incorporating such dependence in the corresponding latent class using the log-linear formulation of the model.
当没有参考测试（金标准）可用时，潜在类别模型（LCMs）可用于评估诊断测试性能，其中考虑代表疾病或非疾病状态的两个潜在类别。此类模型的基本假设之一是局部或条件独立性：所有指标变量（测试）在每个潜在类别内均统计独立。然而，在实际中这一假设常常被违反；因此，双潜在类别模型对数据的拟合效果不佳。在本文中，我们建议使用双标图方法来识别每个潜在类别内一对显变量之间的条件依赖性。此外，我们建议使用模型的对数线性公式将这种依赖性纳入相应的潜在类别中。
