对比贝叶斯学和评分方法,用于对正负概率比的间隔估计,以支持诊断器件性能评估
Tingting Hu1, Berkman Sahiner1, Nicholas Petrick1
1Office of Science and Engineering Laboratories (OSEL), CDRH, USFDA, Silver Spring, Maryland, USA.
Journal of biopharmaceutical statistics
|June 18, 2024
概括
这项研究引入了贝叶斯的方法,用于估计诊断测试准确度指标,如正数和负数概率 (PLR/NLR). 贝叶斯方法提供与得分方法相比较的覆盖概率,但具有更高的精度,以更短的间隔宽度表示.
科学领域:
- 统计 统计 统计 统计
- 医学诊断 医学诊断 医学诊断
- 生物统计学 生物统计学
背景情况:
- 阳性和阴性概率比 (PLR/NLR) 是二进制诊断器件的关键准确度指标.
- 对于PLR/NLR准确性的贝叶斯式和频率式间隔估计器存在有限的研究.
- 本研究研究了PLR/NLR的贝叶斯间隔估计和独立比例的比率.
研究的目的:
- 开发和评估基于贝叶斯的方法,用于PLR/NLR的间隔估计.
- 评估贝叶斯对比频率主义者 (得分) 间隔估计器对独立比例的比率的性能.
- 为了比较不同估计方法的覆盖概率 (CP) 和预期间隔宽度 (EW).
主要方法:
- 开发了贝叶斯的方法,用于间隔估计PLR/NLR和独立比例的比率.
- 进行了广泛的实验,比较贝叶斯和得分间隔估计器.
- 将方法应用于两个案例研究,并对先前的敏感性进行了补充实验.
主要成果:
- 贝叶斯区间估计显示,平均覆盖概率 (CP) 为0.950,相当于得分方法的0.952.
- 贝叶斯方法的总体平均预期间隔宽度 (EW) 较短,为15.929,而得分方法的平均预期间隔宽度为19.724.
- 案例研究应用表明贝叶斯式和频率主义方法之间的间隔估计是相似的.
结论:
- 建议的贝叶斯方法提供了与分数方法进行间隔估计的可比覆盖概率.
- 贝叶斯方法提供了更高的精度,由较短的预期间隔宽度证明.
- 这种贝叶斯方法是诊断设备性能评估和一般比率估计的宝贵工具.
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