Evaluation of accuracy of screening tests in disease diagnosis

研究1：在多阶段筛选研究中将多项试验的结果联合收割机。 总结：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。 研究2.用于比较多个检验与验证偏倚的ROC-GLM模型 连续筛查试验的诊断能力或准确性通常使用真实疾病状态的受试者工作特征（ROC）曲线进行评估，该曲线由确定性金标准试验确定。在实践中，金标准测试可能过于昂贵或 有创性，可定期使用。因此，根据筛选试验的结果，选择研究人群的一个子集以确定疾病状态。在筛选阶段， 筛选测试是用来确定受试者的黄金标准测试的最后工作。这种研究设计通常称为多阶段（阶段）筛选设计。 研究1中使用的方法涉及对认知测试分数分布的假设，这可能是不可检验的验证偏差的存在。在这里，我们考虑一个参数分布的方法比较多个测试的准确性。特别是，我们将参数化ROC曲线的形式，但不会对测试结果的分布做出任何额外的假设。 我们使用广义线性模型（GLM）来估计存在验证偏差的多个测试的ROC曲线。GLM-ROC模型可用于比较多阶段诊断研究中多种筛查试验的准确性，例如，AGES-RS研究。

Study 1: 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. Study 2. ROC-GLM model for comparing multiple tests with verification bias The diagnostic capability or accuracy of an continuous screening test is often assessed using a receiver operating characteristic (ROC) curve using the true disease status, which is ascertained by a deterministic gold standard test. In practice, a gold-standard test may be too expensive or too invasive for regular use. As a result, a subset of the study population is selected to ascertain the disease status, based on the results of screening test. In the screening stage, one or multiple screening tests are used to identify the subjects for final work up of gold standard test. This study design is usually called multi-stage (phase) screening design. The approach used in Study 1 involves assumption on the distribution of the cognitive tests scores, which may be untestable in the presence of verification bias. Here we consider a parametric distribution-free method for comparing the accuracies of multiple tests. In particular, we will parameterize the form the ROC curve but will not make any additional assumptions about the distributions of test results. We use a generalized linear model (GLM) to estimate the ROC curves of multiple tests in the presence of verification bias. The GLM-ROC model is useful to compare the accuracy of multiple screening tests in a multi-stage diagnosis study, e.g., AGES-RS study.