对于混合控制分析的场景加权功率先验,具有时间到事件数据
Evan Kwiatkowski1, Jiawen Zhu2, Xiao Li2
1Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, 1200 Pressler St, Houston, TX 77030, USA.
本研究引入了一种新的混合分析方法,用于使用外部控制的随机对照试验 (RCT). 该方法通过调整患者差异来稳健整合外部数据,增强统计能力.
科学领域:
- 生物统计学 生物统计学
- 临床试验设计 临床试验设计
- 卫生经济学 卫生经济学
背景情况:
- 随机对照试验 (RCT) 是评估治疗疗效的黄金标准.
- 在RCT中,内部控制臂可能因样本大小和患者特征而受到限制.
- 外部控制数据提供了增强RCT的潜力,但需要仔细处理系统差异.
研究的目的:
- 开发一种新的混合分析方法,将RCT中的内部和外部控制数据结合起来.
- 允许灵活借用基于患者相似性的外部控制的信息.
- 当外部数据与RCT数据不完全兼容时,提供可靠的统计推断.
主要方法:
- 一个功率先验方法被扩展到计算每个外部控制器的数据依赖性折扣权重.
- 通过评估外部对照患者和RCT数据之间的兼容性,使用预测分布来确定权重.
- 对于时间到事件分析,采用比例危险回归模型,具有零碎常数基线危险.
主要成果:
- 拟议的病例加权权力先验方法在模拟和现实世界非小细胞肺癌试验中证明了强大的推断.
- 该方法有效地解释了RCT和外部对照群体之间的系统差异,包括未测量的混因素.
- 可调节的借贷强度确保可靠增加内部控制武器.
结论:
- 混合分析方法提供了一个统计学上合理的方法,以利用RCT中的外部控制数据.
- 这种方法提高了临床试验评估的统计能力和效率.
- 这种方法在内部控制小组较小或有外部数据源可用时特别有用.
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