Stat Med. 2010 Oct 30;29(24):2532-43. doi: 10.1002/sim.4018.
Verification bias arises in diagnostic test evaluation studies when the results from a first test are verified by a reference test only in a non-representative subsample of the original study subjects. This occurs, for example, when inclusion probabilities for the subsample depend on first-stage results and/or on a covariate related to disease status. Reference standard bias arises when the reference test itself has imperfect sensitivity and specificity, but this information is ignored in the analysis. Reference standard bias typically results in underestimation of the sensitivity and specificity of the test under evaluation, since subjects that are correctly diagnosed by the test can be considered as misdiagnosed owing to the imperfections in the reference standard. In this paper, we describe a Bayesian approach for simultaneously addressing both verification and reference standard bias. Our models consider two types of verification bias, first when subjects are selected for verification based on initial test results alone, and then when selection is based on initial test results and a covariate. We also present a model that adjusts for a third potential bias that arises when tests are analyzed assuming conditional independence between tests, but some dependence exists between the initial test and the reference test. We examine the properties of our models using simulated data, and then apply them to a study of a screening test for dementia, providing bias-adjusted estimates of the sensitivity and specificity.
在诊断性试验评估研究中，如果仅在原始研究对象的非代表性亚组中，通过参考试验来验证第一次试验的结果，就会出现验证偏倚。例如，当亚组的纳入概率取决于第一阶段的结果和/或与疾病状态相关的协变量时，就会出现这种情况。当参考标准本身的灵敏度和特异性不完美时，就会出现参考标准偏倚，但在分析中忽略了这一信息。参考标准偏倚通常会导致评估中的测试灵敏度和特异性被低估，因为由于参考标准的不完美，那些被测试正确诊断的患者可能会被误诊。在本文中，我们描述了一种贝叶斯方法，用于同时解决验证偏倚和参考标准偏倚。我们的模型考虑了两种类型的验证偏倚，一种是当根据初始测试结果单独选择验证对象时，另一种是当选择基于初始测试结果和协变量时。我们还提出了一种模型，用于调整在假设测试之间存在条件独立性但初始测试和参考测试之间存在一些依赖性的情况下分析测试时出现的第三种潜在偏倚。我们使用模拟数据检验了我们模型的性质，然后将其应用于痴呆症筛查测试的研究中，提供了灵敏度和特异性的偏倚调整估计值。
