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: 8048932
• 负责人: Qingxia Chen
• 金额: $ 19.43万
• 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 2012-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8048932
• 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

DESCRIPTION (provided by applicant): 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 misevaluation 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 application addresses the following four problems. (Aim 1:) The development of a novel doubly robust estimator 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 disease 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. PUBLIC HEALTH RELEVANCE: Screening tests for disease rely on commonly accepted measures that represent each test's "gold standard" to diagnose true disease status. The gold standard test may, however, be too expensive or too invasive to consider implementing for every subject in a study. Verification bias may arise when the verification of the disease status depends on the result of the screening test. This application proposes to develop novel doubly robust estimators to evaluate the accuracy and efficiency of the screening tests in the presence of verification bias.

描述（由申请人提供）：新的诊断测试很快开发出来，现有的诊断测试经常在被引入实践后迅速改进。不幸的是，对测试的统计特性的不准确和偏见的评估通常是设计不佳或分析不足的研究的结果，这会导致其过早的传播，并使用不可靠的测试来做出重要的治疗决策。诊断测试的明显价值的最常见原因也许是验证偏差。当对患者疾病状况的验证取决于拟议的检测结果或与疾病状况相关的某些患者特征的结果时，就会发生验证偏见。正确验证偏差的统计方法很少使用，并且很少使用，并且该应用程序提出了一种新颖的统计策略，用于解决验证偏差，该验证偏差可推广可用于非统计学家（具有适当的软件包）。即使只有一部分低风险的负面筛查患者才能进行侵入性或昂贵的疾病验证，拟议的方法仍将产生有效（且效率高）的策略，以评估所考虑的诊断测试的统计特性。具体而言，此应用程序解决了以下四个问题。 （AIM 1 :)开发新型的双重鲁棒估计量，以在存在验证偏差的情况下使用敏感性，特异性以及正面和负预测值。估计量的稳健性是在某种意义上是，如果实际疾病状态的模型或验证状态的模型（但不一定是两者）是正确的，那么实际估计值是正确的（即一致）。 （AIM 2 :)将目标1中开发的方法扩展到产生连续或顺序结果的测试和生物标志物，以及接收器操作员特征曲线下的面积用于测量诊断精度。 （AIM 3 :)我们“逆转”我们在存在验证偏见的情况下从患者的特征和诊断中开发一种模型来开发模型的方法。最后，这项拟议的研究的临床意义是广泛的，因为本质上是诊断性的。这些方法具有改善诊断测试的统计评估的巨大潜力，这反过来又可以显着提高我们的医生进行准确诊断的能力。 公共卫生相关性：疾病的筛查测试取决于代表每个测试的“黄金标准”的普遍措施来诊断真正的疾病状况。但是，黄金标准测试可能太昂贵或过于侵入性，无法考虑为研究中的每个主题实施。当疾病状态的验证取决于筛查测试的结果时，可能会出现验证偏差。该应用建议在存在验证偏置的情况下开发新颖的双重稳健估计器，以评估筛选测试的准确性和效率。