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连续诊断测试的Youden指数的间隔估计,具有验证偏差的数据
Shirui Wang1, Shuangfei Shi1, Gengsheng Qin1
1Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, USA.
Statistical methods in medical research
|March 20, 2025
概括
本研究介绍了在医学诊断测试中估计尤登指数的偏差校正方法,以解决缺失的疾病状态数据. 对于未知的疾病模型,推使用启动-SPE间隔,提供强大的性能.
科学领域:
- 生物统计学 生物统计学
- 医学诊断 医学诊断 医学诊断
- 统计建模 统计建模
背景情况:
- 尤登指数对于评估诊断测试的有效性和最佳值选择至关重要.
- 由于疾病状况数据不完整而导致的验证偏差,可能会导致Youden指数估计器偏差.
- 现有的偏差校正方法主要集中在接收器运行特征 (ROC) 曲线上,而不是尤登指数.
研究的目的:
- 为连续诊断测试的尤登指数提出新的偏差校正间隔估计方法.
- 为了应对在失踪随机假设下部分缺失疾病状况数据的挑战.
- 使用已建立的偏差校正技术,为尤登指数开发置信区间.
主要方法:
- 调整了阿隆佐和佩佩的四个估计器 (完全归算,平均得分归算,反向概率加权,半参数效率).
- 启动重新抽样和差异估计恢复方法 (MOVER) 被应用来构建置信区间.
- 为了评估性能,进行了广泛的模拟和真实数据分析.
主要成果:
- 当疾病模型被正确指定时,MOVER-FI间隔显示出优越的覆盖概率.
- 低的验证比例存在一个权衡:引导式方法提供更高的准确性,而MOVER方法提供更高的精度.
- 引导式-SPE间隔显示出强大的双重稳定性,用于模型错误规范.
结论:
- 当真正的疾病模型由于其强度而未知时,建议使用Bootstrap-SPE间隔.
- 当真正的疾病模型可以准确近似时,建议使用MOVERws-FI间隔.
- 该研究为在存在验证偏差的情况下准确估计尤登指数提供了有价值的工具.
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