Evaluation of biomarkers and screening tests in the disease diagnosis

疾病诊断中生物标志物的评价和筛选试验

• 批准号: 7969914
• 负责人: Binbing Yu
• 金额: $ 40.49万
• 依托单位: NATIONAL INSTITUTE ON AGING
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7969914
• 关键词: Age; Aging; Appearance; Bayesian Method; Biological Markers; Body Composition; Brain; Brain imaging; Clinical Research; Cognitive; Colorectal; Creatine; Data; Dementia; Dependence; Diagnosis; Diagnostic; Diagnostic tests; Disease; Economics; Education; Educational Background; Elderly; Environment; Epidemiologic Studies; Ethics; Evaluation; Functional Imaging; Gold; Health; Image; Imaging technology; Kidney Diseases; Methods; Modeling; Neurodegenerative Disorders; Paper; Phase; Physiological; Population; Predisposition; Prevalence; Process; ROC Curve; Reader; Renal function; Sampling; Scheme; Screening procedure; Sensitivity and Specificity; Serum; Site; Specificity; Staging; Statistical Methods; Testing; Work; aging gene; design; disease diagnosis; efficacy testing; high risk; malignant breast neoplasm; middle age; older patient; outcome forecast; population based

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.

研究1：比较两种没有黄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，经常对同一受试者或样本进行多项诊断测试，以评估这些测试的准确性，并估计疾病患病率。然而，通常情况下，出于经济或伦理方面的考虑，金本位制可能并不容易获得。在这种情况下，缺乏可识别性是评估测试有效性的常见挑战。已经提出了潜变量模型和贝叶斯方法，用于在没有金标准的情况下估计诊断测试的准确性和疾病流行率。在这项工作中，我们扩展了先前提出的二元检验的贝叶斯方法，通过回归模型对患病率和准确性对协变量的依赖关系进行建模，从而允许包含关于协变量的信息。作为应用，该方法被应用于一项基于人口老龄化研究的数据。我们结合了三种流行的GFR测试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的估计和测试的准确性添加重要信息。 研究2：在多阶段筛查研究中结合多项测试的结果。 摘要：在确定真实疾病状态的研究中，确定的诊断测试通常太具侵入性或太昂贵，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。第一阶段测试的所有受试者的结果都被用来确定哪些受试者将在稍后阶段接受黄金标准测试。第一阶段测试是廉价和非侵入性的。仅限于核实的案例分析会导致核实偏差。多阶段设计已被用于痴呆症的研究以及许多其他疾病的诊断和筛查，例如结直肠癌和乳腺癌。设计通常涉及两个以上的阶段。例如，在三个阶段的研究中，第一阶段的流行检测通常具有较高的敏感性，但相对较低的特异性；第二阶段包括第二次应用筛查测试或更具验证性的测试；最后阶段的测试是金标准。在本文中，我们提出了一种在多阶段研究中估计检验有效性参数和连续筛选试验的ROC曲线的方法。筛查试验的验证过程和有效性也可能取决于协变量。在调整验证偏差后，我们评估了检验有效性参数的估计，并通过实证研究比较了不同的序贯检验组合方案。 如果我们假设患有未经证实的痴呆症的人是非痴呆者，我们倾向于对ROC曲线持乐观态度。例如，对于一个70岁没有受过教育的受试者，使用75的临界值可以得到FPR=0.42，TPR=0.64。如果我们忽略验证偏差，则FPR=0.39，TPR=0.96，这高估了特异度。比较图4a-d，我们发现，如果使用75作为临界值，教育程度对测试精度有显著影响，但不同年龄的差异不大。对于70岁并受过10年教育的受试者，以75岁为分界点，FPR为0.05，TPR为0.30。因此，以75为临界值的筛查测试对受过10年教育的受试者具有较高的敏感性和相对较低的特异性。对于低教育程度的受试者，敏感度和特异度都中等。在ROC曲线下的AUC方面，CASI测试对于受过高等教育的受试者表现得更好。