一个全面的审查和闪亮的应用对匹配调整的间接比较
Ziren Jiang1, Joseph C Cappelleri2, Margaret Gamalo3
1Division of Biostatistics and Health Data Science, University of Minnesota School of Public Health, Minneapolis, Minnesota, USA.
Research synthesis methods
|February 21, 2024
概括
经匹配调整的间接比较 (MAIC) 有助于在没有直接试验的情况下估计治疗的有效性. 本综述详细介绍了MAIC方法,并介绍了Shiny-MAIC,这是一个易于使用的R应用程序,用于进行这些分析.
科学领域:
- 卫生经济学 卫生经济学
- 生物统计学 生物统计学
- 相对有效性研究研究比较
背景情况:
- 对人口调整的间接比较 (PAIC) 在缺乏对比试验的情况下,对治疗有效性的评估至关重要.
- 关键的PAIC方法包括匹配调整间接比较 (MAIC),模拟治疗比较 (STC) 和多层网络元回归 (ML-NMR).
- MAIC使用一个试验的个人参与者数据平衡独立试验之间的共变量.
研究的目的:
- 提供对匹配调整间接比较 (MAIC) 方法的全面审查.
- 讨论MAIC的理论基础,假设和变化 (定与未定).
- 介绍和展示Shiny-MAIC的实用性,这是一个用于执行MAIC分析的新R应用程序.
主要方法:
- 对MAIC方法的理论推导和假设的审查.
- 探索MAIC与调查采样中的校准估计之间的联系.
- 在Shiny-MAIC R应用程序中实施各种MAIC方法,为共变量类型,结果和差异估计器提供选择.
主要成果:
- 这篇文章提供了MAIC的详细理论审查,包括其假设和与校准估计的关系.
- 该Shiny-MAIC应用程序被介绍为第一个R Shiny工具来实现各种MAIC方法.
- 该应用程序支持/无MAIC,各种共同变量/结果,以及以模拟数据证明的引导/强大的标准错误.
结论:
- MAIC 是一种有价值的技术,用于医疗技术评估中的比较有效性研究.
- 闪亮MAIC应用程序提供了一个用户友好的平台,用于进行强大的MAIC分析,以帮助医疗保健决策.
- 这种工具使研究人员和决策者能够民主使用先进的间接比较方法.
相关概念视频
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01:09Wilcoxon Signed-Ranks Test for Matched Pairs
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