内容:ONEST的概括来估计预测增强智能方法验证研究的样本大小.
Benjamin K Olson1, Joseph H Rosenthal2, Ryan D Kappedal3
1University of California Santa Cruz, Santa Cruz, CA, USA.
Journal of pathology informatics
|November 24, 2025
概括
像CONTEST这样的新统计方法可以计算样本大小以验证主观临床试验,特别是当机器学习工具缺乏FDA针对多类问题的指导时. 这有助于对患者护理进行准确的测试验证.
科学领域:
- 临床诊断 临床诊断 临床诊断
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 在报告患者结果之前,对临床实验室来说,测试验证至关重要.
- 目前的FDA指南缺乏用于验证多类机器学习决策支持工具的具体框架.
- 传统的验证指标,如准确性,精度,可报告范围和参考间隔是必不可少的.
研究的目的:
- 引入评估主观测试所需的案例和观察者 (CONTEST),这是ONEST的扩展,用于验证主观测试.
- 用参数概率分布来演示计算测试验证所需样本大小的方法.
- 为验证增强主观测试的工具提供一个框架,特别是在资源有限的环境中.
主要方法:
- 开发了一个治疗效果扩展的观察者需要评估主观测试 (ONEST) 框架,命名为CONTEST.
- 使用参数概率分布的指定同意和不同意分布.
- 根据所需的水平和功率,推导出计算测试验证所需样本大小的方法.
主要成果:
- 证明了用于主观测试验证的样本大小计算可以使用CONTEST进行.
- 表明同意和不同意分布可以通过参数模型来建模.
- 拟议的方法适用于使用现有数据集验证增强主观测试.
结论:
- 在主观测试验验证中,CONTEST提供了一种统计学上合理的方法来计算样本大小.
- 该方法解决了验证多类决策支持工具的差距,特别是那些采用机器学习的工具.
- 在需要验证诊断试验的资源有限的环境中,CONTEST特别有价值.
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