用于估计监测系统的灵敏度的样本大小,该系统生成具有自相关性的重复二进制结果
Albert E Parker1,2, James W Arbogast3,4
1Center for Biofilm Engineering, Montana State University, Bozeman, MT, USA.
Statistical methods in medical research
|November 2, 2023
概括
新的样本大小公式有助于估计自动化手部卫生监测系统的灵敏度. 这些公式考虑相关数据,确保可靠的置信区间,即使在高灵敏度水平.
科学领域:
- 医疗监测系统 医疗监测系统
- 生物统计学 生物统计学
- 感染控制的方法是感染控制.
背景情况:
- 准确估计监控系统的灵敏度对于医疗保健应用至关重要.
- 现有的方法在自相关和聚类时间序列数据方面扎,特别是对于高灵敏度的数据.
- 监测手部卫生的遵守对于预防医疗保健相关感染至关重要.
研究的目的:
- 开发和介绍新的样本大小公式,以估计监测系统的灵敏度,使用自相关和聚类二进制时间序列数据.
- 为了解决更简单的方法在实现高灵敏度的指定的信心水平的局限性.
- 为自动化手部卫生监测系统的设计提供一个强大的统计框架.
主要方法:
- 应用自回归顺序1混合效应物流回归模型的应用.
- 根据所需的置信度和错误率推导样本大小公式.
- 考虑数据特征:时间序列二进制数据,自相关性,并按患者护理单位进行聚类.
主要成果:
- 提供了公式来确定必要的事件和患者护理单位,以估计灵敏度.
- 拟议的模型确保了指定的置信区间,克服了对90%以上灵敏度的简单方法的局限性.
- 该方法已被验证用于自动化手部卫生监测系统.
结论:
- 开发的样本大小公式为设计监测系统提供了统计学上合理的方法.
- 准确的样本大小计算对于在医疗监测中可靠的灵敏度估计至关重要.
- 自动回归混合效应模型为复杂数据结构中的样本大小确定提供了更准确和可靠的方法.
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