Correcting for partial verification bias in diagnostic accuracy studies: A tutorial using R
Correcting for partial verification bias in diagnostic accuracy studies: A tutorial using R
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DOI：
10.1002/sim.9311
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发表时间：
2022-01-18
影响因子：
2
通讯作者：
Yusof，Umi Kalsom
中科院分区：
文献类型：
--
作者：
Arifin，Wan Nor;Yusof，Umi Kalsom
诊断测试在医疗保健中起着至关重要的作用。因此，任何新的诊断测试都必须经过彻底的评估。将新的诊断测试与各自的金标准测试进行比较评估。二元诊断试验的性能是量化的准确性措施，敏感性和特异性是最重要的措施。在任何诊断准确性研究中，由于对患者的选择性验证，这些措施的估计往往有偏倚，这被称为部分验证偏倚。根据指标测试的规模、目标结果和缺失数据机制，可以使用几种方法来纠正部分验证偏差。然而，由于方法的复杂性，研究人员不容易获得这些数据。本文旨在简要概述可用于纠正涉及二元诊断测试的部分验证偏差的方法，并提供有关如何使用统计编程语言R实现这些方法的实用教程。
Diagnostic tests play a crucial role in medical care. Thus any new diagnostic tests must undergo a thorough evaluation. New diagnostic tests are evaluated in comparison with the respective gold standard tests. The performance of binary diagnostic tests is quantified by accuracy measures, with sensitivity and specificity being the most important measures. In any diagnostic accuracy study, the estimates of these measures are often biased owing to selective verification of the patients, which is referred to as partial verification bias. Several methods for correcting partial verification bias are available depending on the scale of the index test, target outcome, and missing data mechanism. However, these are not easily accessible to the researchers due to the complexity of the methods. This article aims to provide a brief overview of the methods available to correct for partial verification bias involving a binary diagnostic test and provide a practical tutorial on how to implement the methods using the statistical programming language R.