Kosinski Andrzej S, Barnhart Huiman X
Department of Biostatistics, The Rollins School of Public Health of Emory University, 1518 Clifton Road, NE, Atlanta, Georgia 30322, USA.
Biometrics. 2003 Mar;59(1):163-71. doi: 10.1111/1541-0420.00019.
A "gold" standard test, providing definitive verification of disease status, may be quite invasive or expensive. Current technological advances provide less invasive, or less expensive, diagnostic tests. Ideally, a diagnostic test is evaluated by comparing it with a definitive gold standard test. However, the decision to perform the gold standard test to establish the presence or absence of disease is often influenced by the results of the diagnostic test, along with other measured, or not measured, risk factors. If only data from patients who received the gold standard test were used to assess the test performance, the commonly used measures of diagnostic test performance--sensitivity and specificity--are likely to be biased. Sensitivity would often be higher, and specificity would be lower, than the true values. This bias is called verification bias. Without adjustment for verification bias, one may possibly introduce into the medical practice a diagnostic test with apparent, but not truly, high sensitivity. In this article, verification bias is treated as a missing covariate problem. We propose a flexible modeling and computational framework for evaluating the performance of a diagnostic test, with adjustment for nonignorable verification bias. The presented computational method can be utilized with any software that can repetitively use a logistic regression module. The approach is likelihood-based, and allows use of categorical or continuous covariates. An explicit formula for the observed information matrix is presented, so that one can easily compute standard errors of estimated parameters. The methodology is illustrated with a cardiology data example. We perform a sensitivity analysis of the dependency of verification selection process on disease.
一种能对疾病状态进行明确验证的“金标准”检测可能具有相当大的侵入性或成本高昂。当前的技术进步提供了侵入性较小或成本较低的诊断检测方法。理想情况下，通过将诊断检测与明确的金标准检测进行比较来评估该诊断检测。然而，决定进行金标准检测以确定疾病是否存在，往往会受到诊断检测结果以及其他已测量或未测量的风险因素的影响。如果仅使用接受金标准检测的患者的数据来评估检测性能，那么诊断检测性能的常用指标——敏感性和特异性——可能会产生偏差。敏感性通常会高于真实值，而特异性会低于真实值。这种偏差被称为验证偏差。如果不调整验证偏差，可能会在医疗实践中引入一种表面上具有高敏感性但实际上并非如此的诊断检测方法。在本文中，验证偏差被视为一个缺失协变量问题。我们提出了一个灵活的建模和计算框架，用于评估诊断检测的性能，并对不可忽视的验证偏差进行调整。所提出的计算方法可以与任何能够重复使用逻辑回归模块的软件一起使用。该方法基于似然性，允许使用分类或连续协变量。给出了观测信息矩阵的显式公式，以便能够轻松计算估计参数的标准误差。通过一个心脏病学数据示例对该方法进行了说明。我们对验证选择过程对疾病的依赖性进行了敏感性分析。
