A New Approach to Correct Verification Bias Using Auxiliary Information

使用辅助信息纠正验证偏差的新方法

• 批准号: 8207859
• 负责人: Qingxia Chen
• 金额: $ 23.4万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-12-20 至 2014-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8207859
• 关键词: Address; Area; Biological Markers; Characteristics; Clinical; Clinical Research; Computer software; Data; Data Set; Derivation procedure; Development; Diagnosis; Diagnostic; Diagnostic tests; Disease; Disease model; Eating; Equation; Ethics; Evaluation; Gold; Likelihood Functions; Measures; Medicine; Methods; Modeling; Nature; Outcome; Patients; Physicians; Predictive Value; Probability; Property; Receiver Operating Characteristics; Receiver Operator Characteristics; Reporting; Research; Research Personnel; Risk; Sampling; Screening Result; Screening procedure; Sensitivity and Specificity; Specificity; Statistical Methods; Techniques; Test Result; Testing; Translational Research; Weight; Work; base; cohort; cost; design; diagnostic accuracy; improved; novel; novel diagnostics; novel strategies; premature; simulation; tool

Project Summary New diagnostic tests are developed quickly, and existing diagnostic tests are often rapidly improved after being introduced into practice. Unfortunately, inaccurate and biased evaluations of a test's statistical properties, often the result of a poorly designed or poorly analyzed study, leads to their premature dissemination and to physicians using unreliable tests to make critical treatment decisions. Perhaps the most common cause for the misevalu- ation of diagnostic tests is verification bias. Verification bias occurs when the verification of a patient's disease status depends on the result of the proposed test or certain patient characteristics associated with disease status. Statistical methods that correct for verification bias are underdeveloped and seldom used, and this application proposes a novel statistical strategy for addressing verification bias that is generalizable and accessible to non- statisticians (with appropriate software package). Even when only a select subset of low-risk negative-screening patients can undergo invasive or costly disease verification, the proposed method will still yield a valid (and cost- efficient) strategy for evaluating the statistical properties of the diagnostic test under consideration. Specifically, this proposal addresses the following four problems. (Aim 1:) The development of a novel doubly robust esti- mator for sensitivity, specificity, and positive and negative predictive values that can be used in the presence of verification bias. The estimators are doubly robust in the sense that the actual estimate is correct (i.e., consistent) in moderately large samples if either the model for true disease status or the model for verification status (but not necessarily both) is correct. (Aim 2:) To extend the methods developed in Aim 1 to tests and biomarkers that yield continuous or ordinal outcomes and where the area under a receiver operator characteristic curve is used to measure diagnostic accuracy. (Aim 3:) We 'reverse' our approach to develop a model for predicting dis- ease status, from patient's characteristic and diagnosis, in the presence of verification bias.(Aim 4:) To develop and freely distribute an assessable a software package that will implement these methods for statisticians and clinical researchers alike. Finally, the clinical implications of this proposed research are wide-ranging as much of medicine is diagnostic in nature. These methods have great potential to improve the statistical evaluation of diagnostic tests, which will in turn yield significant improvement in the ability of our physicians to make accurate diagnoses.

项目摘要 新的诊断测试开发得很快，现有的诊断测试在被使用后通常会迅速改进。 引入实践。不幸的是，对测试统计特性的不准确和有偏见的评估， 设计不良或分析不良的研究结果，导致其过早传播， 使用不可靠的测试来做出关键的治疗决定。可能米西瓦卢最常见的原因- 诊断测试的评估是验证偏差。当对患者疾病的验证 状态取决于所提出的测试的结果或与疾病状态相关的某些患者特征。 纠正验证偏差的统计方法还不发达，很少使用，这种应用 提出了一种新的统计策略，用于解决验证偏差，这种偏差是可推广的，可以被非 统计员（配备适当的软件包）。即使只有低风险阴性筛查的一个选择子集 患者可以进行侵入性或昂贵的疾病验证，所提出的方法仍然会产生有效的（和成本- 有效的）策略来评估所考虑的诊断测试的统计特性。具体地说， 这一建议解决了以下四个问题。(Aim 1：）一种新型的双重鲁棒估计的发展， 灵敏度、特异性以及阳性和阴性预测值的检验，可用于存在以下情况： 验证偏差估计器在实际估计是正确的（即，一致） 在中等规模的样本中，如果真实疾病状态的模型或验证状态的模型（但 不一定两者都是正确的。(Aim 2：）将目标1中开发的方法扩展到测试和生物标志物 产生连续或有序结果，并且受试者操作特征曲线下面积为 用来衡量诊断的准确性。(Aim 3：）我们“逆转”我们的方法，以开发一个模型来预测疾病- 在存在验证偏倚的情况下，从患者的特征和诊断来评估缓解状态。(Aim 4：）发展 并免费分发一个可评估的软件包，该软件包将为统计人员实施这些方法， 临床研究人员一样。最后，这项研究的临床意义是广泛的， 医学的本质是诊断。这些方法有很大的潜力，以改善统计评价， 诊断测试，这反过来将大大提高我们的医生做出准确诊断的能力。 诊断。