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Weighted generalized score test for comparing predictive values in the presence of verification bias
Statistics in Medicine ( IF 1.8 ) Pub Date : 2022-08-05 , DOI: 10.1002/sim.9540
Yougui Wu 1
Affiliation  

Positive and negative predictive values of a diagnostic test are two important measures of test accuracy, which are more relevant in clinical settings than sensitivity and specificity. Statistical methods have been well-developed to compare the predictive values of two binary diagnostic tests when test results and disease status fully observed for all study patients. In practice, however, it is common that only a subset of study patients have the disease status verified due to ethical or cost considerations. Methods applied directly to the verified subjects may lead to biased results. A bias-corrected method has been developed to compare two predictive values in the presence of verification bias. However, the complexity of the existing method and the computational difficulty in implementing it has restricted its use. A simple and easily implemented statistical method is therefore needed. In this paper, we propose a weighted generalized score (WGS) test statistic for comparing two predictive values in the presence of verification bias. The proposed WGS test statistic is intuitive and simple to compute, only involving some minor modification of the WGS test statistic when disease status is verified for each study patient. Simulations demonstrate that the proposed WGS test statistic preserves type I error much better than the existing Wald statistic. The method is illustrated with data from a study of methods for the diagnosis of coronary artery disease.

中文翻译:

用于在存在验证偏差的情况下比较预测值的加权广义分数测试

诊断测试的阳性和阴性预测值是测试准确性的两个重要指标,在临床环境中比灵敏度和特异性更相关。当所有研究患者的测试结果和疾病状态得到充分观察时,统计方法已经得到很好的发展,可以比较两种二元诊断测试的预测值。然而,在实践中,出于伦理或成本方面的考虑,通常只有一部分研究患者的疾病状态得到验证。直接应用于已验证对象的方法可能会导致有偏差的结果。已经开发了一种偏差校正方法来比较存在验证偏差时的两个预测值。然而,现有方法的复杂性和实现它的计算难度限制了它的使用。因此需要一种简单且易于实施的统计方法。在本文中,我们提出了一种加权广义评分 (WGS) 检验统计量,用于在存在验证偏差的情况下比较两个预测值。所提出的 WGS 测试统计量直观且易于计算,在为每个研究患者验证疾病状态时仅涉及对 WGS 测试统计量的一些小修改。模拟表明,所提出的 WGS 测试统计比现有的 Wald 统计更好地保留了 I 类错误。该方法使用来自冠状动脉疾病诊断方法研究的数据进行说明。所提出的 WGS 测试统计量直观且易于计算,在为每个研究患者验证疾病状态时仅涉及对 WGS 测试统计量的一些小修改。模拟表明,所提出的 WGS 测试统计比现有的 Wald 统计更好地保留了 I 类错误。该方法使用来自冠状动脉疾病诊断方法研究的数据进行说明。所提出的 WGS 测试统计量直观且易于计算,在为每个研究患者验证疾病状态时仅涉及对 WGS 测试统计量的一些小修改。模拟表明,所提出的 WGS 测试统计比现有的 Wald 统计更好地保留了 I 类错误。该方法使用来自冠状动脉疾病诊断方法研究的数据进行说明。
更新日期:2022-08-05
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