Measuring diagnostic test performance using imperfect reference tests: A partial identification approach
利用不完美参照检测衡量诊断性能：部分识别方法AI 解读 ↓
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利用不完美参照检测衡量诊断性能：部分识别方法
- 研究问题
- 当用于确认真实健康状态的参照检测也存在误差时，如何区分表观性能与真实敏感度、特异度，并界定政策相关指标？
- 方法与思路
- 作者在标准设定下推导真实性能的最窄可能界及患病率、预测值的衍生界，利用矩不等式方法构造具有统一性质的置信集。
- 材料陈述的贡献
- 摘要称，方法提供尖锐界；BinaxNOW 应用中，有症状和无症状患者的估计假阴性率最高分别为表观值的三点一七和四点五九倍。
- 阅读关注点
- 应核对参照检测性能的信息来源、两类检测误差关系的限制、界的尖锐性，以及应用中的数据选择和置信集覆盖性质。
- 阅读边界
- 仅依据所给出版社摘要；标准设定的完整条件和估计不确定性未说明，所报倍数是特定应用结果，不能推广至其他检测。
原文摘要
Diagnostic tests are almost never perfect. Studies quantifying their performance use knowledge of the true health status, measured with a reference diagnostic test. Researchers commonly assume that the reference test is perfect, which is often not the case in practice. When the assumption fails, conventional studies identify “apparent” performance or performance with respect to the reference, but not true performance. This paper provides the smallest possible bounds on the measures of true performance — sensitivity (true positive rate) and specificity (true negative rate), or equivalently false positive and negative rates, in standard settings. Implied bounds on policy-relevant parameters are derived: (1) Prevalence in screened populations; (2) Predictive values. Methods for inference based on moment inequalities are used to construct uniformly consistent confidence sets in level over a relevant family of data distributions. Emergency Use Authorization (EUA) and independent study data for the BinaxNOW COVID-19 antigen test demonstrate that the bounds can be very informative. Analysis reveals that the estimated false negative rates for symptomatic and asymptomatic patients are up to 3.17 and 4.59 times higher than the frequently cited “apparent” false negative rate. Further applicability of the results in the context of imperfect proxies such as survey responses and imputed protected classes is indicated.