Evaluation of biomarkers and screening tests in the disease diagnosis

研究1：比较两个没有金标准的竞争性测试的准确性。 摘要：在人群筛查或疾病诊断中，通常对同一受试者或样本进行多种诊断测试，以评估这些测试的准确性并估计疾病患病率。 然而，由于经济或道德方面的考虑，黄金标准往往并不容易获得。在这种情况下，缺乏可识别性是估计测试有效性的常见挑战。潜变量模型和贝叶斯方法已被提出用于在缺乏金标准的情况下估计诊断测试和疾病流行的准确性。 在这项工作中，我们扩展了先前提出的贝叶斯方法进行二进制测试，允许通过建模的依赖性的患病率和准确性的协变量通过回归模型的协变量的信息。 作为一个应用程序，该方法被应用到从人口为基础的老龄化研究的数据。我们结合了三种流行的GFR检测来估计老年人肾功能受损的患病率，并证明协变量可以为患病率和检测准确性的估计增加重要信息。 研究2：在多阶段筛选研究中将多项试验的结果联合收割机。 摘要：在确定真实疾病状态的研究中，确定性诊断测试通常过于侵入性或昂贵，无法应用于所有受试者，在这种情况下，通常使用两阶段设计。第1阶段测试的所有受试者的结果，这是廉价和非侵入性的，用于确定哪些受试者将在以后的阶段接受金标准测试。分析仅限于已验证的病例，导致验证偏倚。 多阶段设计已用于痴呆的研究以及许多其他疾病的诊断和筛选，例如，结肠直肠癌和乳腺癌。 设计通常涉及两个以上的阶段。例如，在三阶段研究中，第一阶段的流行测试通常具有高灵敏度，但特异性相对较低;第二阶段包括第二次应用筛选测试或更确证的测试;最后阶段的测试是金标准。本文提出了一种在存在验证偏倚的多期连续筛选试验中估计试验有效性参数和ROC曲线的方法。筛选试验的验证过程和有效性也可能取决于协变量。我们评估了验证偏差调整后的测试有效性参数的估计，我们比较了不同的方案相结合的序贯检验使用实证研究。 如果我们假设未经证实的痴呆状态的人为非痴呆，我们倾向于对ROC曲线持乐观态度。 例如，对于70岁且未受教育的受试者，使用75的截止值产生FPR=0.42和TPR=0.64。如果忽略验证偏倚，则FPR=0.39，TPR=0.96，高估了特异性。比较图4a-d，我们发现，如果使用75岁的截止值，教育水平对测试准确性有显著影响，但不同年龄的差异不大。对于70岁且受教育年限为10年的受试者，对于75岁的临界值，FPR为0.05，TPR为0.30。因此，对于受教育年限为10年的受试者，使用75分作为临界值的筛查试验具有较高的敏感性和相对较低的特异性。对于受教育程度低的受试者，敏感性和特异性均为中等。在ROC曲线下的AUC方面，CASI检验在高等教育受试者中表现更好。

Study 1: Compare the accuracies of two competitive tests without a gold standard. Summary: In population screening or disease diagnosis, multiple diagnostic tests are often conducted on the same subject or sample in order to evaluate the accuracy of these tests and to estimate disease prevalence. Oftentimes, due to economic or ethical concerns, a gold standard may not be readily available, however. The lack of identifiability is a common challenge for estimating test efficacy in this situation. Latent variable models and Bayesian methods have been proposed for estimating the accuracy of diagnostic tests and disease prevalence in the absence of a gold standard. In this work, we extended the previously proposed Bayesian method for binary tests to allow the inclusion of information on covariates by modeling the dependence of prevalence and accuracy on covariates via regression models. As an application, the method was applied to data from a population-based aging study. We combined three popular tests of GFR to estimate the prevalence of impaired kidney function among older adults and to demonstrate that covariates can add important information to the estimation of prevalence and test accuracy. Study 2: Combine the results from multiple tests in a multi-stage screening study. Summary: In studies to ascertain true disease status, a definitive diagnostic test often is too invasive or expensive to be applied to all subjects, in which case a two-phase design is often used. The results for all subjects from the Phase 1 test, which is inexpensive and non-invasive, are used to determine which subjects will receive the gold standard test in a later phase. Analysis restricted only to verified cases leads to verification bias. The multiple phase design has been used in studies of dementia and in the diagnosis and screening of many other diseases, e.g., colorectal and breast cancer. The design usually involves more than two phases. For example, in a three-phase study the prevalent test in Phase 1 usually has high sensitivity, but relatively low specificity; Phase 2 consists of a second application of the screening test or a more confirmatory test; and the test in the final phase is the gold standard. In this paper, we proposed a method of estimating the parameters of test efficacy and the ROC curves for continuous screening tests in a multiple-phase study in the presence of verification bias. The verification process and efficacy of the screening test could also depend on covariates. We evaluated estimates of parameters of test efficacy after adjusting for verification bias, and we compared different schemes for combining the sequential tests using empirical studies. If we assume the people with unverified dementia status as non-demented, we tend to be optimistic about the ROC curve. For example for a subject who is 70 years old with no education, using a cut-off at 75 yields FPR=0.42 and TPR=0.64. If we ignore the verification bias, then FPR=0.39 and TPR=0.96, which over-estimates the specificity. Comparing Figure4 a-d, we see that education level has a remarkable impact on test accuracy if the cut-off of 75 is used, but there is not much difference for different ages. For the subject who is 70 years old and has 10 years of education, the FPR is 0.05 and TPR is 0.30 for the cut-off of 75. So the screening test using the cut-off of 75 has a high sensitivity and relatively low specificity for subjects with a 10-year education. For subjects with low education, both sensitivity and specificity are moderate. In terms of AUC under the ROC curve, the CASI test performed better for subjects with higher education.