存在分类协变量时两种二元诊断试验预测值的同时比较
Simultaneous comparison of the predictive values of two binary diagnostic tests in the presence of categorical covariates
- University of Granada（格拉纳达大学）
- University of Nouakchott Alaasriya（努瓦克肖特阿拉萨里亚大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种全局假设检验，通过回归模型和加权最小二乘法，在存在分类协变量时同时比较两种二元诊断试验的预测值，模拟显示回归模型方法渐近行为更优且功效更高。
AI中文摘要：
诊断试验预测值的比较是医学统计学中一个备受关注的话题，并已成为多项研究的主题。在临床实践中，比较诊断试验时经常观察到分类协变量。在此框架下，本文提出了一种全局假设检验，用于在所有个体均观察到分类协变量的情况下，同时比较两种诊断试验的预测值。该假设检验通过回归模型以及用于分类数据分析的加权最小二乘法来求解。进行了模拟实验，以研究在观察到二元协变量和观察到具有三个类别的协变量时这些方法的渐近行为，并将其与忽略协变量时全局检验的行为进行比较。总的来说，基于回归模型的方法显示出比其他方法更好的渐近行为。此外，我们研究了在没有观察到协变量时应用基于回归模型的方法，此时样本中的个体被随机分配到一个二元虚拟随机变量。进行的模拟实验表明，该方法比没有协变量的方法具有更大的功效。结果应用于两个示例。
英文摘要：
Comparison of predictive values of diagnostic tests is a topic of interest in Medical Statistics, and has been the subject of different studies. In clinical practice, it is frequent to observe categorical covariates when comparing diagnostic tests. In this framework, a global hypothesis test is proposed to simultaneously compare the predictive values of two diagnostic tests when in all of the individuals categorical covariates are observed. This hypothesis test is solved through regression models and also by weighted least squares method for the analysis of categorical data. Simulation experiments were carried out to study the asymptotic behavior of these methods when a binary covariate is observed and when a covariate with three categories is observed, and these were compared to the behavior of the global test when the covariate is ignored. In general, the method based on regression models has shown to have better asymptotic behavior than the other methods. Furthermore, we studied the application of the method based on the regression models when no covariate is observed, for which the individuals in the sample are randomly assigned to a binary dummy random variable. Simulation experiments carried out showed that this method has greater power than the method without the covariate. The results were applied to two examples.
