Department of Biostatistics, Colorado School of Public Health, University of Colorado, Denver, Aurora, CO, USA.
BMC Med Res Methodol. 2010 Jan 11;10:3. doi: 10.1186/1471-2288-10-3.
Cancer screening reduces cancer mortality when early detection allows successful treatment of otherwise fatal disease. There are a variety of trial designs used to find the best screening test. In a series screening trial design, the decision to conduct the second test is based on the results of the first test. Thus, the estimates of diagnostic accuracy for the second test are conditional, and may differ from unconditional estimates. The problem is further complicated when some cases are misclassified as non-cases due to incomplete disease status ascertainment.
For a series design, we assume that the second screening test is conducted only if the first test had negative results. We derive formulae for the conditional sensitivity and specificity of the second test in the presence of differential verification bias. For comparison, we also derive formulae for the sensitivity and specificity for a single test design, both with and without differential verification bias.
Both the series design and differential verification bias have strong effects on estimates of sensitivity and specificity. In both the single test and series designs, differential verification bias inflates estimates of sensitivity and specificity. In general, for the series design, the inflation is smaller than that observed for a single test design.The degree of bias depends on disease prevalence, the proportion of misclassified cases, and on the correlation between the test results for cases. As disease prevalence increases, the observed conditional sensitivity is unaffected. However, there is an increasing upward bias in observed conditional specificity. As the proportion of correctly classified cases increases, the upward bias in observed conditional sensitivity and specificity decreases. As the agreement between the two screening tests becomes stronger, the upward bias in observed conditional sensitivity decreases, while the specificity bias increases.
In a series design, estimates of sensitivity and specificity for the second test are conditional estimates. These estimates must always be described in context of the design of the trial, and the study population, to prevent misleading comparisons. In addition, these estimates may be biased by incomplete disease status ascertainment.
癌症筛查可降低癌症死亡率,因为早期发现可成功治疗原本致命的疾病。有多种试验设计用于寻找最佳的筛查测试。在系列筛查试验设计中,进行第二次测试的决定是基于第一次测试的结果。因此,第二次测试的诊断准确性估计是有条件的,可能与无条件估计不同。当由于不完全的疾病状态确定而将某些病例错误分类为非病例时,问题会变得更加复杂。
对于系列设计,我们假设仅当第一次测试结果为阴性时才进行第二次筛查测试。我们推导出存在差异验证偏差时第二次测试的条件敏感性和特异性的公式。作为比较,我们还推导出具有和不具有差异验证偏差的单次测试设计的敏感性和特异性的公式。
系列设计和差异验证偏差都会对敏感性和特异性的估计产生强烈影响。在单次测试和系列设计中,差异验证偏差都会夸大敏感性和特异性的估计。一般来说,对于系列设计,膨胀幅度小于单次测试设计。偏差的程度取决于疾病流行率,错误分类病例的比例以及病例的测试结果之间的相关性。随着疾病流行率的增加,观察到的条件敏感性不受影响。但是,观察到的条件特异性呈上升的偏倚。随着正确分类病例的比例增加,观察到的条件敏感性和特异性的向上偏差减小。随着两个筛查测试之间的一致性增强,观察到的条件敏感性的向上偏差减小,而特异性偏差增加。
在系列设计中,第二次测试的敏感性和特异性估计是有条件的估计。这些估计必须始终根据试验设计和研究人群的情况进行描述,以防止产生误导性的比较。此外,这些估计可能会因不完全的疾病状态确定而存在偏差。