Bias correction optimizes the choice of screening test for early cancer detection

描述（由申请人提供）： 癌症筛查用于早期发现癌症。筛查的目标是发现更多的癌症，避免错误诊断的痛苦和折磨。随着新的癌症筛查测试的开发，它们与现有的模式进行了比较。在配对试验中比较筛选试验的最常见的统计方法是检查受试者工作特征（ROC）曲线下面积之间的差异，这是敏感性和特异性的汇总指标。然而，这种方法可能会导致严重的偏见，导致错误的决定，以筛选测试更好。偏倚的产生是因为某些癌症在临床上是隐匿性的，在试验期间未观察到。这夸大了筛查试验的观察灵敏度。如果两个筛选试验的灵敏度有差异地膨胀，则偏倚会恶化。这发生在不同比例的参与者与疾病被称为活检的每一个筛选测试。由于增加活检率或对设计进行其他更改在伦理上是不可能的，因此需要新的统计方法来纠正这种偏倚。我们提出了新的技术来测试配对筛选试验中存在的偏差，提供无偏的估计灵敏度，特异性和曲线下面积，并允许正确的决定，筛选测试具有最佳的诊断准确性。该方法的实用性将使用Lewin等人的数据进行证明，2002年的一项试验，比较了数字和胶片乳腺X线摄影的诊断准确性。出版物、演示文稿和交互式软件将使医生、流行病学家、统计学家和其他研究设计者能够访问我们的研究结果。无偏见的临床试验将确定具有最佳诊断准确性的筛查测试。反过来，更准确的筛查测试将改善癌症检测，减少假阴性诊断，并最终降低癌症发病率和死亡率。

DESCRIPTION (provided by applicant): Cancer screening is used for the early detection of cancer. The goal in screening is to detect more cancers and avoid the pain and suffering of incorrect diagnoses. As new cancer screening tests are developed, they are compared to existing modalities. The most common statistical approach for comparing screening tests in paired trials is to examine the difference between the areas under the receiver operating characteristic (ROC) curves, a summary measure of both sensitivity and specificity. However, this approach may cause bias severe enough to lead to the wrong decision as to which screening test is better. The bias arises because some cancer is clinically occult, and is not observed during the trial period. This inflates the observed sensitivity of the screening tests. The bias is worsened if the sensitivity is inflated differentially for the two screening tests being compared. This occurs when a different fraction of participants with disease are referred to biopsy by each screening test. Because increasing the rate of biopsy or making other changes in the design is ethically impossible, new statistical methods are needed to correct for this bias. We propose new techniques to test for the presence of bias in paired screening trials, to provide unbiased estimates of sensitivity, specificity, and area under the curve, and to allow correct decisions as to which screening test has the best diagnostic accuracy. The utility of the methods will be demonstrated using data from the Lewin et al., 2002 trial, a comparison of the diagnostic accuracy of digital and film mammography. Publications, presentations and interactive software will make our findings accessible to physicians, epidemiologists, statisticians and other study designers. Unbiased clinical trials will identify the screening tests with the best diagnostic accuracy. In turn, more accurate screening tests will improve cancer detection, reduce false negative diagnoses, and ultimately reduce cancer morbidity and mortality.