Leisenring W, Pepe M S, Longton G
Division of Public Health Sciences and Clinical Statistics, Fred Hutchinson Cancer Research Center, Seattle, Washington 98104, USA.
Stat Med. 1997 Jun 15;16(11):1263-81. doi: 10.1002/(sici)1097-0258(19970615)16:11<1263::aid-sim550>3.0.co;2-m.
Technological advances continue to develop for early detection of disease. Research studies are required to define the statistical properties of such screening or diagnostic tests. However, statistical methodology currently used to evaluate diagnostic tests is limited. We propose the use of marginal regression models with robust sandwich variance estimators to make inference about the sensitivity and specificity of diagnostic tests. This method is more flexible than standard methods in that it allows comparison of sensitivity between two or more tests even if all tests are not carried out on all subjects, it can accommodate correlated data, and the effect of covariates can be evaluated. This last feature is important since it allows researchers to understand the effects on sensitivity and specificity of various environmental and patient characteristics. If such factors are under the control of the clinician, it provides the opportunity to modify the diagnostic testing program to maximize sensitivity and/or specificity. We show that the marginal regression modelling methods generalize standard statistical methods. In particular, when we compare two screening tests and we test each subject with both screens, the method corresponds to McNemar's test. We describe data from an ongoing audiology screening study and we analyse a simulated version of the data to illustrate the methodology. We also analyse data from a longitudinal study of PCR as a diagnostic test for cytomegalovirus.
疾病早期检测的技术进步仍在不断发展。需要开展研究来界定此类筛查或诊断测试的统计学特性。然而，目前用于评估诊断测试的统计方法存在局限性。我们建议使用带有稳健三明治方差估计量的边际回归模型来推断诊断测试的灵敏度和特异度。该方法比标准方法更为灵活，因为即使并非对所有受试者都进行了所有测试，它也能比较两个或更多测试之间的灵敏度，它可以处理相关数据，并且能够评估协变量的效应。最后这一特性很重要，因为它使研究人员能够了解各种环境和患者特征对灵敏度和特异度的影响。如果此类因素处于临床医生的控制之下，那么就有机会修改诊断测试方案，以最大限度地提高灵敏度和/或特异度。我们表明，边际回归建模方法推广了标准统计方法。特别是，当我们比较两种筛查测试并且对每个受试者都用两种筛查方法进行检测时，该方法相当于 McNemar 检验。我们描述了一项正在进行的听力筛查研究的数据，并分析了该数据的模拟版本以阐述该方法。我们还分析了一项关于将聚合酶链反应作为巨细胞病毒诊断测试的纵向研究的数据。
