Direct estimation of volume under the ROC surface with verification bias.

IF 1.2 4区 医学 Q4 PHARMACOLOGY & PHARMACY Journal of Biopharmaceutical Statistics Pub Date : 2024-07-03 Epub Date: 2023-07-20 DOI:10.1080/10543406.2023.2236202
Shuangfei Shi, Gengsheng Qin
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Abstract

In practice, the receiver operating characteristic (ROC) curve of a diagnostic test is widely used to show the performance of the test for discriminating two-class events. The area under the ROC curve (AUC) is proposed as an index for the assessment of the diagnostic accuracy of the test under consideration. Due to ethical and cost considerations associated with application of gold standard (GS) tests, only a subset of the patients initially tested have verified disease status. Statistical evaluation of the test performance based only on test results from subjects with verified disease status are typically biased. Various AUC estimation methods for tests with verification biased data have been developed over the last few decades. In this article, we develop new direct estimation methods for the volume under the ROC surface (VUS) by extending the AUC estimation methods for two-class diagnostic tests to three-class diagnostic tests in the presence of verification bias. The proposed methods will provide a comprehensive guide to deal with the verification bias in three-class diagnostic test accuracy studies and lead to a better choice of diagnostic tests.

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带验证偏差的 ROC 表面下体积直接估算。
在实践中,诊断测试的接收者操作特征曲线(ROC)被广泛用于显示测试在区分两类事件方面的性能。ROC 曲线下的面积(AUC)被认为是评估测试诊断准确性的指标。出于应用金标准(GS)检验的伦理和成本考虑,只有一部分最初接受检验的患者的疾病状态得到了验证。仅根据已确认疾病状态的受试者的检测结果对检测性能进行统计评估通常会有偏差。在过去的几十年中,针对存在验证偏差数据的检验,已经开发出了各种 AUC 估算方法。在本文中,我们通过将两类诊断测试的 AUC 估算方法扩展到存在验证偏倚的三类诊断测试,开发了新的 ROC 面下容积(VUS)直接估算方法。所提出的方法将为处理三类诊断检测准确性研究中的验证偏差提供全面指导,从而更好地选择诊断检测。
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来源期刊
Journal of Biopharmaceutical Statistics
Journal of Biopharmaceutical Statistics 医学-统计学与概率论
CiteScore
2.50
自引率
18.20%
发文量
71
审稿时长
6-12 weeks
期刊介绍: The Journal of Biopharmaceutical Statistics, a rapid publication journal, discusses quality applications of statistics in biopharmaceutical research and development. Now publishing six times per year, it includes expositions of statistical methodology with immediate applicability to biopharmaceutical research in the form of full-length and short manuscripts, review articles, selected/invited conference papers, short articles, and letters to the editor. Addressing timely and provocative topics important to the biostatistical profession, the journal covers: Drug, device, and biological research and development; Drug screening and drug design; Assessment of pharmacological activity; Pharmaceutical formulation and scale-up; Preclinical safety assessment; Bioavailability, bioequivalence, and pharmacokinetics; Phase, I, II, and III clinical development including complex innovative designs; Premarket approval assessment of clinical safety; Postmarketing surveillance; Big data and artificial intelligence and applications.
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