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在后勤和生存分析中改进预测增量措施的估计
Danielle M Enserro1, Austin Miller1
1Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14263, USA.
Cancers
|April 26, 2025
概括
百分位启动区间为AUC,NRI和IDI提供可靠的置信区间估计,在大多数场景中通过保持覆盖范围和宽度,优于非对称方法. 这有助于改善歧视改善评估.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 对AUC,NRI和IDI的置信区间估计至关重要但具有挑战性.
- 由于正常分布假设问题,非对称方法可能是无效的.
- 引导提供了一个强有力的估计的潜在替代方案.
研究的目的:
- 为了比较AUC,NRI和IDI的置信区间的异常和启动方法.
- 在后勤和生存回归背景下评估绩效.
- 确定最可靠的歧视改善评估方法.
主要方法:
- 检查了AUC (ΔAUC),NRI和IDI变化的性能.
- 利用后勤和生存回归模型.
- 进行模拟以比较非对称和启动信任区间的覆盖概率.
主要成果:
- 百分位启动信任区间在物流和生存框架中表现出强的表现.
- 这些间隔可以实现良好的覆盖范围,而不会影响宽度.
- 除了在强烈效应大小的情况下,非对称间隔的可靠性较低.
结论:
- 建议使用百分位启动间隔来可靠地估计歧视改进指标.
- 非对称间隔仅适用于强效果大小.
- 这些发现旨在提高估计和评估歧视改善的准确性.
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