两个模型的比较,用于检测网络元分析中的不一致性
Lu Qin1, Shishun Zhao1, Wenlai Guo2
1Center for Applied Statistical Research and College of Mathematics, Jilin University, Changchun, China.
Research synthesis methods
|July 4, 2024
概括
在网络元分析 (NMA) 中检测不一致性对于可靠的临床指导至关重要. 设计逐处理交互模型与侧面分割模型相比,在各种数据结构中提供了强大的不一致性检测.
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
- 卫生研究方法论 卫生研究方法论
背景情况:
- 网络元分析 (NMA) 越来越多地用于合成来自多种治疗比较的证据.
- 确保NMA中直接和间接证据之间的一致性对于可靠的临床决策至关重要.
- 不一致性检测是NMA的关键步骤,用于验证结果用于临床指导的使用.
研究的目的:
- 综合审查和探索设计后处理交互模型和NMA中不一致性检测的侧面分割模型之间的关系.
- 使用频率主义方法在不同的数据结构下比较这些模型的性能.
- 提供关于在NMA中选择合适的模型进行不一致性评估的实际指导.
主要方法:
- 这项研究审查了NMA不一致性的两个主要模型:设计-通过-处理交互模型和侧面分割模型.
- 使用频率主义方法来分析这些模型,将NMA数据结构视为缺失数据.
- 进行分析和数值研究,以探索模型的关系和性能.
主要成果:
- 侧面分割模型被确定为设计后处理交互模型的特定实例,适用于某些数据结构或额外假设.
- 设计逐处理交互模型在检测不同数据结构中的不一致性方面表现出优越而强大的性能,与侧面分割模型相比.
- 该研究通过数据结构参数化和分析证实了模型之间的关系.
结论:
- 在NMA中推设计逐处理交互模型用于一般不一致性检测,特别是当不一致性的位置未知时.
- 侧面分割模型可以成为详细不一致性评估的有价值的补充工具,特别是在较小的网络中或在检查本地不一致性时.
- 这项研究为理解和应用不同的NMA不一致性检测方法提供了一个框架,以改进证据合成.
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