用极端价值理论估计诊断测试的截止值,以实现目标特异性
Sierra Pugh1, Bailey K Fosdick2, Mary Nehring3
1Department of Statistics, Colorado State University, 102 Statistics Building, Fort Collins, 80523, Colorado, USA.
BMC medical research methodology
|February 8, 2024
概括
选择最佳的诊断测试截止值对于新出现的疾病至关重要. 极端价值理论为使用有限数据进行高特异性测试提供了一个强大的方法,其性能优于传统方法.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 诊断测试开发 诊断测试开发
背景情况:
- 准确的诊断测试对于监测新出现的疾病至关重要,特别是在流行病发病率低的早期阶段.
- 高特异性至关重要,以尽量减少假阳性,但选择适当的测试截止值与有限的验证数据是具有挑战性的.
- 在新诊断测试的背景下,估计量子的现有统计方法可能对极端值不理想.
研究的目的:
- 提出和评估一种使用极端价值理论的新方法,用于选择具有预先确定的特异性的诊断测试截止值.
- 为了比较基于极端价值理论的方法与现有的切断选择技术的性能.
- 评估不同特异性目标对测试准确性和流行率估计的影响.
主要方法:
- 利用极值理论,将帕雷托分布与负对照数据的上尾相匹配,以确定最佳的切断值.
- 将拟议的方法与之前建议的其他五种切断选择方法进行了比较.
- 进行了数据分析和模拟研究,使用COVID-19酶相关免疫吸收试验抗体测试结果.
主要成果:
- 极端值方法在针对0.995.99的高特异性时显示出最小的偏差.
- 经验量子式方法在0.95.5的特异性目标上表现良好.
- 较高的目标特异性在低患病率场景中提高了整体测试准确性,而较低的特异性在更高患病率场景中降低了患病率估计变异性.
结论:
- 极端价值理论和经验定量方法优于以正常为基础的方法来确定具有有限训练数据的疾病测试截止值.
- 在诊断测试开发中,建议基于极端值的方法用于高特异性目标,而经验量则用于低特异性目标.
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