灵活的定量偏差分析,用于在主体级间接治疗比较中未测量的混与比例危险的违规行为
Steven Soutar1, Amy Macdougall2, Jamie Wallis2
1Arcturis Data, Building One, Oxford Technology Park, Technology Drive, Oxford, OX5 1GN, UK. steven.soutar@arcturisdata.com.
BMC medical research methodology
|May 10, 2025
概括
本研究引入了一种定量偏差分析 (QBA) 框架,用于评估间接治疗比较中未测量的混. 该方法准确地调整偏差,即使有比例危险违规,加强了新疗法的证据.
科学领域:
- 生物统计学 生物统计学
- 卫生经济学 卫生经济学
- 临床试验方法论 临床试验方法论
背景情况:
- 间接治疗比较 (ITC) 对于新疗法有效性至关重要,当RCT不可行时.
- 信息通信技术容易受到未测量的混,可能导致偏见的结果.
- 现有的定量偏差分析 (QBA) 方法在违反比例危险 (PH) 时经常失败.
研究的目的:
- 开发和验证ITC的QBA框架,特别是解决PH违规问题.
- 量化受限平均生存时间 (dRMST) 对未测量的混的敏感性.
- 评估ITC结论的可靠性. 评估ITC结论的可靠性.
主要方法:
- 提出了一个基于模拟的QBA框架,使用贝叶斯数据增强来进行未测量的混因子归算.
- 调整dRMST使用权重分析与归算的混值.
- 通过模拟评估基于归算的调整准确性,并应用于与PH违规的外部控制臂研究.
主要成果:
- 基于归算的调整方法在估计调整后的dRMST中显示出最小的偏差,与完全混调整相比.
- 该QBA框架成功地确定了未测量的混因子的特征,这些特征可能会取消研究结论.
- 这种方法证明有效,即使有显著的PH违规.
结论:
- 拟议的QBA框架在没有测量的混的情况下准确地恢复调整后的dRMST.
- 这一框架使得ITC,特别是那些有PH违规的ITC,能够进行可靠的敏感性分析.
- 它提高了来自ITC的相对疗效证据的可靠性.
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