对个体参与者数据进行元分析,以在多个时间点检查线性或非线性治疗-共变相互作用,以获得连续的结果
Miriam Hattle1,2, Joie Ensor1,2, Katie Scandrett1,2
1National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, Birmingham, UK.
这项研究引入了一种新的多变量元分析方法,用于个人参与者数据 (IPD),以建模随时间推移的复杂治疗-共变相互作用. 这种方法提高了连续结果的精度,特别是在骨关节炎的运动干预中.
科学领域:
- 生物统计学 生物统计学
- 临床试验 临床试验
- 流行病学 流行病学
背景情况:
- 个人参与者数据 (IPD) 的元分析对于识别治疗效果修饰剂至关重要.
- 现有的方法难以与非线性关系和多个时间点进行连续的结果.
- 对治疗-共变体相互作用的统计建模是必不可少的.
研究的目的:
- 提出一个两阶段的多变量IPD元分析方法.
- 总结跨多个时间点的非线性治疗-共变相互作用函数,以获得连续的结果.
- 在IPD元分析中处理缺失的数据.
主要方法:
- 一种两阶段的方法,涉及受限立方线和多变量随机效应元分析.
- 在设置阶段识别关键时间点,节点位置和一个共同的参考组.
- 从单个试验中联合合成参数估计,并考虑相关性.
主要成果:
- 多变量方法有效地总结了多个时间点的非线性相互作用.
- 它可以适应试验内或跨试验的缺失结果.
- 在骨关节炎运动干预IPD元分析中证明了精度的提高.
结论:
- 拟议的方法为分析IPD元分析中的复杂相互作用提供了一个强大的框架.
- 它提供了简要的非线性相互作用与置信区间的图形显示.
- 提高精度,并处理跨多个时间点连续结果的挑战.
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