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需要解决测量误差的层次模型来评估治疗效果和对照组事件率之间的相关性
M Hassan Murad1, Haitao Chu2, Zhen Wang1
1Evidence-based Practice Center, Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA.
Journal of clinical epidemiology
|March 20, 2024
概括
一个新的层次模型 (HM) 解决了元分析中的测量误差,在28%的研究中揭示了治疗效果与对照事件率的显著关联. 这突出了不同基线风险之间治疗效果可移植性的潜在问题.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 医学研究方法学 医学研究方法学
背景情况:
- 分析经常面临测量误差和数学合的挑战,可能会对治疗效应的估计产生偏见.
- 权重线性回归 (WLR) 是一种常见的方法,但容易受到这些问题的影响.
- 了解治疗效果和对照事件率 (CR) 之间的关系对于评估治疗可移植性至关重要.
研究的目的:
- 应用一个层次模型 (HM),以计算CR处理效应回归中的测量误差.
- 为了比较HM与WLR在分析大规模元分析数据中的表现.
- 在元分析中提供可访问的开源代码来实现HM.
主要方法:
- 审查了设计用于处理测量错误的现有层次模型 (HM).
- 在开源软件中实现了HM的贝叶斯版本.
- 使用来自Cochrane系统审查数据库的3193个元分析的大样本比较HM和WLR,包括33,071项研究.
主要成果:
- 在17.19%的元分析中,HM和WLR都确定了治疗效果和CR之间的统计学上显著的关联.
- 在另外11.28%的元分析中,HM发现了显著的关联,而WLR没有.
- 与WLR相比,HM通常会产生较宽的置信区间和距离零更远的斜率.
结论:
- 大约28%的元分析显示,在使用HM来解决测量错误时,治疗效果和CR之间存在显著的关联.
- 这表明,在不同基线风险之间,相对治疗效应的可移植性经常受到限制.
- 开源代码可用,以促进元分析师使用HM.
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