Accounting for nonignorable verification bias in assessment of diagnostic tests
Accounting for nonignorable verification bias in assessment of diagnostic tests
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DOI：
10.1111/1541-0420.00019
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发表时间：
2003-03-01
期刊：
影响因子：
1.9
通讯作者：
Barnhart, HX
中科院分区：
文献类型：
--
作者：
Kosinski, AS;Barnhart, HX
提供疾病状态的明确验证的“金”标准测试可能是相当侵入性的或昂贵的。目前的技术进步提供了侵入性更小或更便宜的诊断测试。理想情况下，通过将诊断测试与确定的金标准测试进行比较来评估诊断测试。然而，进行金标准测试以确定是否存在疾病的决定通常受到诊断测试结果以及其他测量或未测量的风险因素的影响，沿着其他测量或未测量的风险因素。如果仅使用接受金标准试验的患者的数据来评估试验性能，则诊断试验性能的常用测量灵敏度和特异性可能存在偏倚。灵敏度通常高于真实值，特异性低于真实值。这种偏差被称为验证偏差。如果不对验证偏倚进行调整，人们可能会在医疗实践中引入一种具有明显但并非真正高灵敏度的诊断测试。在这篇文章中，验证偏差被视为一个缺失的协变量问题。我们提出了一个灵活的建模和计算框架，用于评估诊断测试的性能，并调整不可重复的验证偏差。所提出的计算方法可以与可以重复使用逻辑回归模块的任何软件一起使用。该方法是基于可能性的，并允许使用分类或连续协变量。给出了观测信息矩阵的一个显式公式，从而可以方便地计算估计参数的标准误差。该方法与心脏病学数据的例子说明。我们进行了灵敏度分析的依赖性的验证选择过程中的疾病。
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.