优化最小可检测变化统计的准确性和精度:从NIH工具箱研究中对测试-重新测试数据的二次分析
Jeremy Graber1,2, Brian J Loyd3,4, Thomas J Hoogeboom5
1Eastern Colorado VA Health Care System, Geriatric Research Education and Clinical Center (GRECC), Aurora, CO, United States.
Physical therapy
|November 22, 2024
概括
传统的最小可检测变化 (MDC) 计算可能不准确. 基于回归的方法 (MDCSLR,MDCGAMLSS) 为随着时间的推移监测患者功能提供了更高的准确性和精度.
科学领域:
- 临床测量 临床测量
- 生物统计学 生物统计学
- 康复科学 康复科学 康复科学
背景情况:
- 最小可检测变化 (MDC) 统计数据对于监测患者进展至关重要.
- 传统的MDC计算方法可能缺乏准确性和精度,可能导致临床错误.
- 这项研究评估了传统MDC计算的基于回归的替代方案.
研究的目的:
- 为了比较传统的MDC计算 (MDCTrad) 与两个基于回归的方法 (MDCSLR,MDCGAMLSS) 的准确性和精度.
- 评估这些方法在不同测量级别和初始测试值的性能.
主要方法:
- 分析了步行速度 (n=169) 和握力 (n=178) 评估的测试重复测试数据.
- 三种MDC计算方法进行了比较:MDCTrad,简单线性回归 (MDCSLR) 和通用添加模型 (MDCGAMLSS).
- 在所有测量级别和在中位数以上/以下的初始值中评估了准确性和精度.
主要成果:
- 这三种方法都准确地建模了平均化时可检测的变化值.
- MDCTrad显示初始测试值在中位数以下或以上的不准确性.
- 基于回归的方法显著提高了精度:MDCSLR提高了12% (步行速度) 和3% (抓地力);MDCGAMLSS提高了16% (步行速度) 和21% (抓地力).
结论:
- 基于回归的方法 (MDCSLR和MDCGAMLSS) 提供比MDCTrad.更准确和精确的可检测变化值.
- 在本次分析中,MDCGAMLSS表现最好.
- 提高精度和准确性可以帮助临床医生及时检测功能变化,并自信地评估改善.
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