实施多层网络元回归以获得时间到事件结果:复发性耐火多发性髓瘤的案例研究
Dylan Maciel1, Jeroen P Jansen1, Sven L Klijn2
1PRECISIONheor, Evidence Synthesis and Decision Modeling, Vancouver, BC, Canada.
概括
多级网络元回归 (ML-NMR) 有效地比较使用个人患者数据和汇总数据的多种治疗方法. 这项研究表明,与其他治疗方法相比,idecabtagene vicleucel在多发性骨髓瘤中改善了整体存活率.
科学领域:
- 生物统计学 生物统计学
- 临床试验分析
- 药物经济学 药物经济学
背景情况:
- 多级网络元回归 (ML-NMR) 整合了个体患者数据 (IPD) 和随机对照试验 (RCT) 的综合数据.
- 它允许对多种治疗方法进行可靠的比较疗效评估,同时考虑到研究之间的异质性.
- 本研究重点是将ML-NMR应用于时间到事件结果.
研究的目的:
- 为了提供ML-NMR的概述,以获得时间到事件的结果.
- 在复发性/耐药性多发性髓瘤的案例研究中应用ML-NMR.
- 为了证明ML-NMR与R代码的实施.
主要方法:
- 评估了idecabtagene vicleucel,selinexor+dexamethasone,belantamab mafodotin和常规护理的整体生存时间.
- 结合单臂试验和现实世界的数据,形成人工RCT (aRCTs).
- 使用了根据先前的治疗线路,三级耐火状态和年龄进行调整的ML-NMR模型,将模型与留出一项信息标准进行比较.
主要成果:
- 韦布尔的ML-NMR模型证明了最好的合适性.
- 相比于塞利尼克索+德甲,贝兰塔马布 (belantamab mafodotin) 和常规治疗,Idecabtagene vicleucel的整体存活率更高.
- 三级耐火状态是唯一显著的预后因素;其他效果修饰剂的影响最小.
结论:
- 在复杂的治疗网络中,ML-NMR是一种有价值的时间到事件结果分析方法.
- 该研究提供了实用的R代码,以促进ML-NMR的实施.
- 鼓励从业人员使用ML-NMR进行需要调整人口的治疗比较.
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