错误分类对单样和双样样本测试的样本大小的影响,使用二进制终点
Péter Hársfalvi1,2, Jenő Reiczigel1
1Department of Biostatistics, University of Veterinary Medicine Budapest, Budapest, Hungary.
Journal of biopharmaceutical statistics
|December 26, 2024
概括
在研究设计中忽视二进制数据错误分类会降低统计能力. 本研究提供了样本大小公式和R函数,以便在研究规划期间调整错误分类 (灵敏度和特异性),确保足够的功率.
科学领域:
- 生物统计学 生物统计学
- 统计方法 统计方法
- 流行病学 流行病学
背景情况:
- 对二进制数据的分析越来越多地包含错误分类方法.
- 研究设计经常忽略潜在的错误分类,因为缺乏样本大小公式和软件.
- 在设计过程中忽视错误分类可能会导致显著的功耗损失,如果只在分析过程中解决.
研究的目的:
- 强调在设计阶段分析二进制数据的研究中调整样本大小以避免错误分类的必要性.
- 为使用二进制终点的研究提供一个实用的样本大小计算程序,考虑错误分类.
- 为了说明错误分类对一个样本和两个样本测试所需样本大小的影响.
主要方法:
- 对二进制终点的一样本和两样本测试的样本大小公式的开发,包括错误分类.
- 实施样本大小程序作为R函数.
- 基于假定的二项式参数,所需功率,灵敏度 (Se) 和特异性 (Sp) 的样本大小计算.
主要成果:
- 错误分类在一个样本和两个样本测试场景中显著影响所需的样本大小.
- 开发的R函数为研究人员提供了一个工具,用于计算适当的样本大小.
- 将样本大小与错误分类和没有错误分类进行比较,突出了可能造成功耗损失的可能性.
结论:
- 通过适当的样本大小调整,将错误分类纠正纳入研究设计阶段至关重要.
- 提供的方法和R函数可以帮助研究人员避免功耗损失,并设计更强大的研究.
- 精确估计灵敏度和特异性对于在错误分类的情况下有效计算样本大小至关重要.
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