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Updated: May 27, 2025

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An R-Based Landscape Validation of a Competing Risk Model
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对风险预测模型验证的样本信息计算的预期值
Mohsen Sadatsafavi1, Andrew J Vickers2, Tae Yoon Lee1
1Respiratory Evaluation Sciences Program, Collaboration for Outcomes Research and Evaluation, Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, BC, Canada.
概括
信息价值方法量化了风险预测模型的外部验证研究的价值. 这种方法为设计研究提供了基于价值的观点,通过计算从验证数据中预期的临床效益.
科学领域:
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
- 临床流行病学 临床流行病学
背景情况:
- 在临床采用之前,对风险预测模型的外部验证对于评估新种群的性能至关重要.
- 目前的样本大小计算依赖于经典的推断统计数据,这可能对诸如净收益 (NB) 等临床实用性指标不那么重要.
- 信息价值 (VOI) 方法提供了一个框架,以基于临床实用性的预期收益来量化验证数据的价值.
研究的目的:
- 定义和提出用于验证样本信息预期值 (EVSI) 的计算算法.
- 通过模拟研究来评估不同EVSI计算算法的性能.
- 将EVSI计算应用于设计外部验证研究的现实世界案例研究.
主要方法:
- 定义验证EVSI作为从特定大小的验证样本中在NB中预期的收益.
- 开发并比较了关于准确性和速度的三种EVSI计算算法.
- 利用心肌梗塞死亡风险模型,使用非美国试验子集进行验证,以计算美国人口样本大小.
主要成果:
- 模拟研究显示,各种算法中的EVSI值是可比的,其数值准确度和计算时间各不相同.
- 对于2%的风险值,通过1000个观察结果进行验证,获得了0.00101 (真正单位) 或0.04938 (假正单位) 的EVSI.
- 美国每年人口EVSI估计为806个真实阳性结果或39500个错误阳性结果;超过4000次观察后观察到的回报率下降.
结论:
- 信息价值方法为外部验证研究提供了投资的定量回报.
- EVSI提供一种以价值为基础的方法来补充设计预测分析验证研究的传统方法.
- 该框架支持有关外部验证最佳样本大小的知情决策.
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