网络元分析与个人参与者级数据的时间到事件结果使用考克斯回归
Kaiyuan Hua1, Daniel Wojdyla2, Anthony Carnicelli3
1Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.
Statistics in medicine
|February 18, 2025
概括
使用考克斯模型的个人参与者数据 (IPD) 网络元分析 (NMA) 改善了干预效应估计. 本综述比较了IPD-NMA的Cox模型规格,有助于解释危险比率和异质性.
科学领域:
- 生物统计学 生物统计学
- 临床流行病学临床流行病学
- 网络元分析 网络元分析
背景情况:
- 个人参与者级数据 (IPD) 通过能够对共变量调节效应进行可靠评估,从而增强网络元分析 (NMA).
- 准确估计干预效应和置信区间是通过结合多个临床试验的共同变量相关性来促进的.
- 考克斯回归是IPD-NMA中时间到事件结果的合适方法,但缺乏对模型规格的全面审查.
研究的目的:
- 综合审查和比较IPD-NMA的考克斯模型规格和假设.
- 检查不同建模方法对危险比率解释,效果调节和试验异质性的影响.
- 为解释和报告IPD-NMA结果提供实际指导.
主要方法:
- 检查IPD-NMA的各种Cox模型,比较模拟试验,治疗和共变量效应的方法.
- 使用图形工具和统计测试来评估比例危险假设及其影响.
- 探索扩展的考克斯模型用于违反比例危险假设的情况.
- 进行模拟研究以比较模型性能.
- 使用真实数据示例的方法说明.
主要成果:
- 不同的Cox模型规范的比较及其对NMA关键输出的影响.
- 对评估和处理违反比例危险假设的方法的评估.
- 通过现实世界的数据示例来展示实际应用.
结论:
- 本综述为IPD-NMA提供了考克斯模型的批判性比较,为模型选择和解释提供了指导.
- 了解模型假设,特别是比例危险,对于准确估计干预效应和异质性至关重要.
- 这些发现有助于研究人员进行和报告可靠的IPD-NMA研究.
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