Leisenring W, Alonzo T, Pepe M S
Program in Clinical Statistics, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA.
Biometrics. 2000 Jun;56(2):345-51. doi: 10.1111/j.0006-341x.2000.00345.x.
Positive and negative predictive values of a diagnostic test are key clinically relevant measures of test accuracy. Surprisingly, statistical methods for comparing tests with regard to these parameters have not been available for the most common study design in which each test is applied to each study individual. In this paper, we propose a statistic for comparing the predictive values of two diagnostic tests using this paired study design. The proposed statistic is a score statistic derived from a marginal regression model and bears some relation to McNemar's statistic. As McNemar's statistic can be used to compare sensitivities and specificities of diagnostic tests, parameters that condition on disease status, our statistic can be considered as an analog of McNemar's test for the problem of comparing predictive values, parameters that condition on test outcome. We report on the results of a simulation study designed to examine the properties of this test under a variety of conditions. The method is illustrated with data from a study of methods for diagnosis of coronary artery disease.
诊断试验的阳性预测值和阴性预测值是衡量试验准确性的关键临床相关指标。令人惊讶的是，对于最常见的研究设计（即将每个试验应用于每个研究个体），尚无用于比较试验这些参数的统计方法。在本文中，我们提出了一种用于使用这种配对研究设计比较两种诊断试验预测值的统计量。所提出的统计量是从边际回归模型导出的得分统计量，与麦克尼马尔统计量有一定关系。由于麦克尼马尔统计量可用于比较诊断试验的敏感性和特异性（即基于疾病状态的参数），而我们的统计量可被视为针对比较预测值问题（即基于试验结果的参数）的麦克尼马尔检验的类似物。我们报告了一项模拟研究的结果，该研究旨在检验该检验在各种条件下的特性。通过一项关于冠状动脉疾病诊断方法研究的数据说明了该方法。
