Shan Guogen, Amei Amei, Young Daniel
Epidemiology and Biostatistics Program, Department of Environmental and Occupational Health, School of Community Health Sciences, University of Nevada Las Vegas, Las Vegas, NV 89154, USA.
Department of Mathematical Sciences, University of Nevada Las Vegas, Las Vegas, NV 89154, USA.
Comput Math Methods Med. 2015;2015:128930. doi: 10.1155/2015/128930. Epub 2015 Aug 20.
Sensitivity and specificity are often used to assess the performance of a diagnostic test with binary outcomes. Wald-type test statistics have been proposed for testing sensitivity and specificity individually. In the presence of a gold standard, simultaneous comparison between two diagnostic tests for noninferiority of sensitivity and specificity based on an asymptotic approach has been studied by Chen et al. (2003). However, the asymptotic approach may suffer from unsatisfactory type I error control as observed from many studies, especially in small to medium sample settings. In this paper, we compare three unconditional approaches for simultaneously testing sensitivity and specificity. They are approaches based on estimation, maximization, and a combination of estimation and maximization. Although the estimation approach does not guarantee type I error, it has satisfactory performance with regard to type I error control. The other two unconditional approaches are exact. The approach based on estimation and maximization is generally more powerful than the approach based on maximization.
灵敏度和特异度常被用于评估具有二元结果的诊断试验的性能。已提出 Wald 型检验统计量来分别检验灵敏度和特异度。在存在金标准的情况下，Chen 等人（2003 年）基于渐近方法研究了两种诊断试验在灵敏度和特异度非劣效性方面的同时比较。然而，正如许多研究中所观察到的，渐近方法可能存在 I 型错误控制不令人满意的问题，尤其是在中小样本情况下。在本文中，我们比较了三种同时检验灵敏度和特异度的无条件方法。它们是基于估计、最大化以及估计与最大化相结合的方法。虽然估计方法不能保证 I 型错误，但在 I 型错误控制方面具有令人满意的性能。另外两种无条件方法是精确的。基于估计与最大化的方法通常比基于最大化的方法更具功效。
