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.
Stat Med. 2003 Sep 15;22(17):2711-21. doi: 10.1002/sim.1517.
Current advances in technology provide less invasive or less expensive diagnostic tests for identifying disease status. When a diagnostic test is evaluated against an invasive or expensive gold standard test, one often finds that not all patients undergo the gold standard test. The sensitivity and specificity estimates based only on the patients with verified disease are often biased. This bias is called verification bias. Many authors have examined the consequences of verification bias and have proposed bias correction methods based on the assumption of independence between disease status and election for verification conditionally on the test result, or equivalently on the assumption that the disease status is missing at random using missing data terminology. This assumption may not be valid and one may need to consider adjustment for a possible non-ignorable verification bias resulting from the non-ignorable missing data mechanism. Such an adjustment involves ultimately uncheckable assumptions and requires sensitivity analysis. The sensitivity analysis is most often accomplished by perturbing parameters in the chosen model for the missing data mechanism, and it has a local flavour because perturbations are around the fitted model. In this paper we propose a global sensitivity analysis for assessing performance of a diagnostic test in the presence of verification bias. We derive a region of all sensitivity and specificity values consistent with the observed data and call this region a test ignorance region (TIR). The term 'ignorance' refers to the lack of knowledge due to the missing disease status for the not verified patients. The methodology is illustrated with two clinical examples.
当前技术的进步为识别疾病状态提供了侵入性较小或成本较低的诊断测试。当针对侵入性或昂贵的金标准测试评估一种诊断测试时，人们常常发现并非所有患者都接受了金标准测试。仅基于已确诊疾病患者得出的灵敏度和特异度估计值往往存在偏差。这种偏差称为验证偏差。许多作者研究了验证偏差的后果，并基于疾病状态与根据测试结果有条件地选择验证之间的独立性假设，或者等效地基于使用缺失数据术语的疾病状态随机缺失假设，提出了偏差校正方法。这个假设可能不成立，人们可能需要考虑对由不可忽略的缺失数据机制导致的可能不可忽略的验证偏差进行调整。这样的调整最终涉及无法检验的假设，并且需要进行灵敏度分析。灵敏度分析最常通过在所选择的缺失数据机制模型中扰动参数来完成，并且它具有局部性，因为扰动是围绕拟合模型进行的。在本文中，我们提出一种全局灵敏度分析方法，用于评估存在验证偏差时诊断测试的性能。我们推导出与观察数据一致的所有灵敏度和特异度值的区域，并将该区域称为测试无知区域（TIR）。术语“无知”指的是由于未经验证的患者疾病状态缺失而导致的知识缺乏。通过两个临床实例对该方法进行了说明。
