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（申请人提供）：新的诊断测试开发迅速，现有的诊断测试在引入实践后往往得到快速改进。不幸的是，对测试统计特性的不准确和有偏见的评估通常是设计不当或分析不当的研究的结果，导致其过早传播，并导致医生使用不可靠的测试来做出关键的治疗决策。也许诊断测试错误评估的最常见原因是验证偏差。当患者疾病状态的验证取决于所提议的测试的结果或与疾病状态相关的某些患者特征时，就会出现验证偏差。纠正验证偏差的统计方法尚未开发且很少使用，本申请提出了一种新的统计策略来解决验证偏差，该策略可推广并可供非统计学家使用（使用适当的软件包）。即使只有一部分低风险阴性筛查患者可以接受侵入性或昂贵的疾病验证，所提出的方法仍然会产生有效（且具有成本效益）的策略来评估所考虑的诊断测试的统计特性。具体而言，本申请解决以下四个问题。 （目标 1：）开发一种新颖的双稳健估计器，用于敏感性、特异性以及阳性和阴性预测值，可在存在验证偏差的情况下使用。如果真实疾病状态的模型或验证状态的模型（但不一定两者都正确），则估计量在中等大样本中的实际估计是正确的（即一致）的意义上是双重稳健的。 （目标 2：）将目标 1 中开发的方法扩展到产生连续或有序结果的测试和生物标志物，并使用接受者操作特征曲线下的面积来测量诊断准确性。 （目标 3：）我们“扭转”我们的方法，在存在验证偏差的情况下，根据患者的特征和诊断来开发预测疾病状态的模型。（目标 4：）开发并免费分发可评估的软件包，该软件包将为统计学家和临床研究人员实施这些方法。最后，这项拟议研究的临床意义是广泛的，因为许多医学本质上都是诊断性的。这些方法在改善诊断测试的统计评估方面具有巨大的潜力，这反过来又会显着提高我们的医生做出准确诊断的能力。 公共卫生相关性：疾病筛查测试依赖于普遍接受的措施，这些措施代表了每个测试诊断真实疾病状态的“黄金标准”。然而，黄金标准测试可能太昂贵或太具有侵入性，以至于无法考虑对研究中的每个受试者实施。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。该应用建议开发新颖的双稳健估计器，以在存在验证偏差的情况下评估筛选测试的准确性和效率。