在2个阶段的个人参与者数据元分析中估计添加性相互作用
Maartje Basten1,2,3,4, Lonneke A van Tuijl5,6, Kuan-Yu Pan2,7,8
1Department of Health Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
American journal of epidemiology
|September 1, 2024
概括
本研究引入了一种新方法,用于分析个人参与者数据元分析中的添加相互作用. 拟议的三步程序准确地估计了复杂健康结果的相对因相互作用而导致的过度风险 (RERI).
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 个人参与者数据 (IPD) 的元分析增强了研究相互作用和效果修改的能力.
- 添加性相互作用对公共健康更相关,而不是乘法性相互作用,但IPD元分析中缺乏用于二进制或时间到事件结果的既定方法.
- 现有的方法论文献没有充分地解决IPD二阶段元分析中的添加相互作用.
研究的目的:
- 描述一种有效的方法来估计添加相互作用,特别是由于相互作用的相对过度风险 (RERI),在二阶段IPD元分析中.
- 解决直接集中研究水平RERI估计的局限性.
- 为在复杂的流行病学研究中估计附加性相互作用提供实用程序.
主要方法:
- 建议采用三步程序: (1) 在每个研究中估计暴露和产品期效应, (2) 使用多变量元分析进行聚合研究的具体估计,以及 (3) 使用95%置信区间计算整体RERI.
- 该方法使用来自PSY社会因素和癌症 (PSY-CA) 联盟的数据来说明,检查抑郁症和吸烟之间的相互作用与吸烟相关的癌症风险.
- 该程序是为IPD元分析中的二进制或时间到事件结果设计的.
主要成果:
- 拟议的三步程序提供了一个有效的方法,用于估计二阶段IPD元分析中的添加物相互作用 (RERI).
- 直接汇集研究水平RERI估计结果可能会产生无效的结果,突显了对拟议方法的需求.
- 对抑郁症,吸烟和癌症风险的应用证明了该方法的可行性和实用性.
结论:
- 开发的三步程序为IPD元分析中估计附加性相互作用提供了强大的框架,特别是对于二进制和时间到事件数据.
- 这种方法提高了调查与公共卫生相关的效果修改的能力.
- 这些发现对未来的元分析有影响,包括基于已公布数据的元分析.
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