时间对事件结果的未定人口调整间接比较方法,使用反向赔率权重,回归调整和双重稳定方法,使用单个患者或总体数据的双重稳定方法
Julie E Park1, Harlan Campbell2, Kevin Towle1
1PRECISIONheor, Evidence Synthesis and Decision Modeling, Vancouver, BC, Canada.
概括
本研究比较了未定的人口调整间接比较 (PAICs) 的方法,以获得时间到事件的结果. 提出了一种两倍强大的方法,以尽量减少间接治疗比较中的偏差.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 生物统计学 生物统计学
- 药物经济学 药物经济学
背景情况:
- 未定的人口调整间接比较 (PAICs) 对于综合来自不同数据源的证据至关重要.
- 现有的PAIC方法对时间到事件的结果需要进一步调查,以最大限度地减少偏差.
- 个体患者数据 (IPD) 和汇总数据 (AD) 为偏差调整提供了不同的机会.
研究的目的:
- 为时间到事件结果提供未定PAIC方法的概述和比较.
- 评估使用IPD和AD的替代性调整策略.
- 提出和评估一种新的,双重可靠的方法,以尽量减少间接比较中的偏差.
主要方法:
- 在第三线小细胞肺癌的病例研究中,将尼沃卢马布与使用整体存活率的标准护理进行了比较.
- 方法包括IPD-IPD分析 (反向概率加权,回归调整,双重可靠) 和IPD-AD分析 (匹配调整间接比较,模拟治疗比较,双重可靠).
- 最近的一种方法被应用到边缘化条件危险比率的方法比较.
主要成果:
- 尼沃卢马布证明了与标准护理相比的生存率有所改善 (危险比率为0.63-0.69).
- 与基于倾向性得分的分析相比,双重稳定和基于回归的估计显示了稍微更宽的置信区间.
- 纯粹的比较给出了IPD-IPD和IPD-AD分析的相同估计.
结论:
- 对于时间到事件的结果,提出的双重强大的方法可以帮助最大限度地减少模型错误规范的偏差.
- 所有未定的PAIC方法都依赖于包括所有相关的预后共变量的关键假设.
- 需要进一步的研究来验证这些方法在不同的临床环境.
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