一个通用的中断时间序列模型,用于评估复杂的医疗保健干预措施
Maricela Cruz1, Hernando Ombao2, Daniel L Gillen3
1Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA.
我们介绍了通用强大的ITS (GRITS) 模型,用于分析健康结果,特别是二进制和计数数据. 这种新方法增强了复杂干预的中断时间序列 (ITS) 分析.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 生物统计学 生物统计学
- 流行病学 流行病学
背景情况:
- 评估复杂干预对健康结果的影响对于医疗保健和政策至关重要.
- 间断时间序列 (ITS) 设计是回顾性干预影响分析的准实验方法.
- 现有的ITS统计模型主要处理连续结果,限制对离散健康数据的分析.
研究的目的:
- 为分析离散的健康结果 (二进制,计数) 提出通用可靠ITS (GRITS) 模型.
- 扩展ITS方法论,以追随指数式家族分布的结果.
- 为了正式实施离散ITS的变化点测试.
主要方法:
- 开发了针对离散结果的通用可靠ITS (GRITS) 模型.
- 格里茨模型容纳了来自指数分布家族的结果.
- 该方法允许进行变化点估计,多个单位的数据聚合,以及干预前后的比较.
主要成果:
- GRITS模型有效地模拟了二进制并计算了ITS中的健康结果.
- 该方法成功测试并估计了离散ITS数据中的变化点.
- 证明能够借鉴多个单位的信息并评估干预效应.
结论:
- 该GRITS模型显著推进了ITS方法论的离散健康结果.
- 这种方法为评估使用非连续数据的复杂干预提供了一个强大的框架.
- GRITS模型为分析患者跌倒和类似健康事件提供了增强的能力.
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