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Updated: Jul 12, 2025
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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
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通过现实的模拟和广泛的真实世界数据分析,对两个Box-Cox公式进行批判性评估,以确定它们在确定参考区间中的实用性
Kiyoshi Ichihara1, Teppei Yamashita2, Hiromi Kataoka3
1Faculty of Health Sciences, Department of Clinical Laboratory Sciences, Yamaguchi University Graduate School of Medicine, Minami-Kogushi 1-1-1, Ube, 755-0001, Japan.
Computer methods and programs in biomedicine
|October 23, 2023
概括
两个参数的Box-Cox (2pBC) 方法,特别是2pBCopt算法,准确计算参考间隔 (RIs) 并确定分析物分布形状. 这种方法克服了单参数Box-Cox (1pBC) 和非参数方法可靠临床实验室测试的局限性.
科学领域:
- 临床化学 临床化学
- 生物统计学 生物统计学
- 实验室医学 实验室医学
背景情况:
- 参考间隔对于解释实验室测试结果至关重要.
- 理想情况下,对RI的参数估计需要参考值 (RV) 的高斯分布.
- 现有的Box-Cox转换 (1pBC,2pBC) 在准确转换偏斜的RV来计算RI方面存在局限性.
研究的目的:
- 评估一参数 (1pBC) 和两个参数的Box-Cox (2pBC) 公式的技术问题,以确定RI.
- 为RI计算中成功应用2pBC提出一个替代算法.
- 为了比较1pBC,2pBC变体和非参数方法的性能.
主要方法:
- 对1pBC (λ) 和2pBC (λ,α) 的最佳参数是使用逐步线性搜索和网格搜索变量 (2pBCgrid,2pBCopt) 确定的.
- 用模拟的功率正常分布对23种化学分析物的方法准确性和精度进行了评估.
- 通过对来自全球多中心研究的25个分析对象的776个现实世界数据集进行分析来评估实际实用性.
主要成果:
- 2pBCopt和1pBC在现实数据集中分别实现了82.4%和66.9%的高斯转换.
- 1pBC显示在RI和λ估计中的偏差对于高度倾斜的分布.
- 2pBCopt提供了对分析物特定分布形状 (λ) 的公正预测,与产生可变RI的非参数方法不同.
结论:
- 2pBCopt算法对计算参考间隔非常有效.
- 2pBCopt可以通过估计的λ参数准确地确定分析物分布形状.
- 这种方法为临床实验室的RI确定提供了可靠的替代方案.
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