Gaffikin Lynne, McGrath John, Arbyn Marc, Blumenthal Paul D
JHPIEGO, Baltimore, MD 21231, USA.
Clin Trials. 2008;5(5):496-503. doi: 10.1177/1740774508096139.
Verification bias occurs when the percentage of subjects receiving disease verification differs according to the test result. Statistical adjustment yields unbiased sensitivity and specificity under a missing at random (MAR) assumption.
To use an example from an international study to show how the assumptions needed for unbiased statistical adjustment for verification bias can be undermined by conditions on the ground, and that accuracy of estimates is also compromised by too low a sampling fraction of subjects who test negative.
A study in Zimbabwe assessed the accuracy of a screening test for cervical cancer screening, visual inspection with acetic acid (VIA). The study was conducted in two phases, Phase I, where only 10% of subjects with negative tests received verification, and Phase II, in which nearly all subjects were verified. Unadjusted, simple- and covariate-adjusted estimates were compared to investigate factors affecting differences. Bootstrap simulations were used to illustrate the effect of varying test negative sampling fractions.
Phase I unadjusted sensitivity and specificity were 0.66 (0.61-0.70) and 0.34 (0.31-0.36), respectively. Simple-weighted adjusted estimators accounting only for VIA status were 0.20 (0.17-0.23) and 0.80 (0.78-0.81), respectively, suggesting the test to be useless. It was found that verification (colposcopy) capacity in-country had been exceeded, and that random selection of test negative patients for colposcopy had been compromised. Phase II estimates of sensitivity and specificity were 0.77 and 0.64, respectively. With 9% disease prevalence, a VIA test-negative sampling fraction >50% was necessary for the confidence intervals for sensitivity to have more than a 90% probability of including the true value.
Phase I statistical adjustment was not made for MAR deviations unexplained by the two auxiliary factors, Pap results and STD history. Adjustment was not possible for other unmeasured co-factors.
While there are standard formulae for correcting for verification bias, these will be biased if the MAR assumption is not met, which can occur through the actions of study personnel or subjects. Design of such studies in low resource environments needs to either require 100% verification, or employ procedures ensuring that the sample of test negatives who receive verification is indeed random. In addition, required test negative sampling fractions need to incorporate information on both disease prevalence and overall sample size.
当接受疾病验证的受试者百分比因检测结果而异时，就会出现验证偏倚。在随机缺失（MAR）假设下，统计调整可得出无偏倚的灵敏度和特异度。
通过一项国际研究中的实例，说明实地条件如何破坏验证偏倚的无偏统计调整所需的假设，以及检测结果为阴性的受试者抽样比例过低如何影响估计的准确性。
在津巴布韦进行的一项研究评估了宫颈癌筛查的一种检测方法——醋酸肉眼观察法（VIA）的准确性。该研究分两个阶段进行，第一阶段只有10%检测结果为阴性的受试者接受了验证，第二阶段几乎所有受试者都接受了验证。比较了未调整、简单调整和协变量调整后的估计值，以研究影响差异的因素。采用自助法模拟来说明检测结果为阴性的抽样比例变化的影响。
第一阶段未调整的灵敏度和特异度分别为0.66（0.61 - 0.70）和0.34（0.31 - 0.36）。仅根据VIA状态进行简单加权调整后的估计值分别为0.20（0.17 - 0.23）和0.80（0.78 - 0.81），表明该检测方法无用。研究发现，该国的验证（阴道镜检查）能力已超出负荷，随机选择检测结果为阴性的患者进行阴道镜检查受到影响。第二阶段的灵敏度和特异度估计值分别为0.77和0.64。在疾病患病率为9%的情况下，VIA检测结果为阴性的抽样比例>50%，才能使灵敏度的置信区间有超过90%的概率包含真实值。
第一阶段未对两个辅助因素（巴氏涂片结果和性传播疾病病史）无法解释的MAR偏差进行统计调整。对于其他未测量的协变量，无法进行调整。
虽然有校正验证偏倚的标准公式，但如果不满足MAR假设，这些公式将产生偏差，这可能是由于研究人员或受试者的行为导致的。在资源匮乏的环境中进行此类研究的设计，要么需要100%的验证，要么采用确保接受验证的检测结果为阴性的样本确实是随机的程序。此外，所需的检测结果为阴性的抽样比例需要纳入疾病患病率和总体样本量的信息。
