匹配调整间接比较的R包:可用性差异和重复使用先前代码的相同结果
Kurt Taylor1, Anthony J Hatswell2
1Delta Hat Limited, Nottingham, England, UK.
概括
用于匹配调整的间接比较 (MAIC) 的 R 包在很大程度上使用单一的权重计算方法,这可能会影响医疗技术评估. 需要进一步的研究来探索替代方法及其影响.
科学领域:
- 卫生经济学 卫生经济学
- 生物统计学 生物统计学
- 药物经济学 药物经济学
背景情况:
- 经匹配调整的间接比较 (MAIC) 对于从现实数据中合成证据至关重要.
- 评估实施MAIC的软件工具的一致性和可用性对于可靠的医疗技术评估至关重要.
研究的目的:
- 评估用于执行MAICs的R包的结果一致性和可用性.
- 为了确定MAIC结果的潜在变化,由于不同的包实施.
主要方法:
- 利用模拟研究数据集,使用四个R包执行未定MAIC:maic,MAIC,maicplus和maicChecks.
- 在八个场景中计算了权重,使用了不同的总结统计数据 (平均值,中位数,比例).
- 比较有效样本大小 (ESS),加权结果和包可用性,包括对优化算法的探索性分析.
主要成果:
- 所有使用标准代码的包裹都产生了相同的权重,ESS和结果.
- "maicChecks"提供了另一种权重计算,导致ESS高,结果略有不同.
- 可用性各不相同;只有"maic"支持总量级数据介质. 优化算法的变化揭示了结果变化的潜力.
结论:
- 现有的MAIC R包主要依赖于单个重量计算实现.
- 权重计算和优化中的差异可能会影响点估计和不确定性边界.
- 现有和新型MAIC方法的比较研究是有必要的.
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