使用多级回归来确定关联并估计临床麻醉中因患者,从业者和医院或卫生系统实践变化而导致的因果关系
Kazuyoshi Aoyama1,2,3, Alan Yang4, Ruxandra Pinto5
1Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, 555 University Ave, #2211, Toronto, ON, M5G 1X8, Canada. kazu.aoyama@utoronto.ca.
Journal of anesthesia
|September 18, 2024
概括
本教程解释了分析麻醉患者数据的多级回归模型. 它有助于估计因素和临床实践变化如何影响患者的结果.
科学领域:
- 麻醉学 麻醉学
- 临床研究方法 临床研究方法
- 生物统计学 生物统计学
背景情况:
- 临床麻醉数据通常具有多层结构,患者因素嵌入临床医生和医院.
- 传统分析可能无法完全捕捉这些嵌套结构对患者结果的影响.
- 了解临床实践中的变化对于改善患者护理至关重要.
研究的目的:
- 为临床麻醉研究提供多级回归建模技术的教程.
- 展示如何估计不同水平的各种因素对患者结果的影响.
- 解释量化临床实践变化对患者结果影响的方法.
主要方法:
- 介绍用于分析麻醉中嵌套数据的多级回归模型.
- 适用于连续结果的类内相关系数.
- 使用二进制结果的中位数赔率比率来评估实践的变化.
主要成果:
- 多级模型对于有效估计嵌套临床数据中的因素影响至关重要.
- 使用ICC和MOR量化变异性为实践变异提供了洞察力.
- 更好地了解各种因素和做法如何影响患者的治疗结果.
结论:
- 多级回归建模是麻醉研究的一个强大的工具.
- 准确估计因素和实践变异性的影响,提高临床决策.
- 这种方法有助于回答有关患者结果的复杂临床问题.
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