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

描述(由申请人提供)： 癌症筛查用于癌症的早期发现。筛查的目标是发现更多的癌症，避免错误诊断带来的痛苦和痛苦。随着新的癌症筛查测试的开发，它们被与现有的模式进行比较。在配对试验中比较筛查试验最常见的统计方法是检查受试者工作特征(ROC)曲线下的区域之间的差异，ROC曲线是敏感度和特异度的综合衡量标准。然而，这种方法可能会导致严重的偏差，从而导致错误地决定哪种筛查测试更好。这种偏见的产生是因为一些癌症在临床上是隐蔽的，在试验期间没有观察到。这夸大了筛查试验的观察灵敏度。如果被比较的两种筛查试验的敏感度被不同地夸大，偏差就会恶化。这发生在每次筛查试验将不同比例的疾病参与者转诊为活组织检查时。因为增加活检率或在设计中进行其他改变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见。我们提出了新的技术来测试配对筛查试验中是否存在偏差，以提供对灵敏度、特异度和曲线下面积的无偏估计，并允许正确地决定哪种筛查试验具有最好的诊断准确性。这些方法的实用性将通过使用勒文等人2002年的试验数据进行验证，该试验比较了数字乳房X光照相和胶片乳房X光检查的诊断准确性。出版物、演示文稿和互动软件将使医生、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。公正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进癌症检测，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.