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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要新的诊断测试开发迅速，现有的诊断测试在经过改进后往往得到快速改进。引入实践。不幸的是，对测试统计特性的评估常常不准确且有偏见设计不当或分析不当的研究结果会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定。也许造成错误评价的最常见原因是诊断测试的化验存在验证偏差。当验证患者的疾病时会出现验证偏差状态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。纠正验证偏差的统计方法尚未开发且很少使用，并且此应用程序提出了一种新颖的统计策略来解决验证偏差，该策略可推广且可供非统计学家（带有适当的软件包）。即使只有低风险阴性筛查的选定子集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的（和成本- 有效）评估所考虑的诊断测试的统计特性的策略。具体来说，该提案解决了以下四个问题。（目标 1：）开发一种新颖的双鲁棒估计敏感性、特异性以及阳性和阴性预测值的因素，可在存在以下情况时使用验证偏差。从实际估计正确（即一致）的意义上来说，估计量具有双重稳健性在中等大的样本中，如果真实疾病状态的模型或验证状态的模型（但是不一定两者都是正确的。（目标 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的结果，并且接收者算子特征曲线下的面积为用于衡量诊断准确性。（目标 3：）我们“逆转”我们的方法来开发预测问题的模型在存在验证偏差的情况下，根据患者的特征和诊断确定缓解状态。（目标 4：）并免费分发一个可评估的软件包，该软件包将为统计学家和临床研究人员等。最后，这项拟议研究的临床意义是广泛的医学本质上是诊断性的。这些方法在改善统计评估方面具有巨大潜力诊断测试，这反过来将显着提高我们的医生做出准确诊断的能力诊断。