治疗政策估计 估计使用非治疗顺序多重计算的连续结果
Thomas Drury1, Juan J Abellan1, Nicky Best1
1GSK, London, UK.
Pharmaceutical statistics
|August 5, 2024
概括
新的多重归因 (MI) 模型解决了临床试验中治疗中止的偏差. 这些模型通过考虑停止治疗后的数据来改善治疗效应的估计,特别是持续的结果,这与传统方法不同.
科学领域:
- 生物统计学 生物统计学
- 临床试验方法论 临床试验方法论
- 药学研究 药学研究
背景情况:
- 国际协调理事会E9 (R1) 准则强调精确的估计和定义,包括处理间流事件 (IE),如停止治疗.
- 连续重复测量的传统方法,如重复测量的混合模型 (MMRM) 和多重归算 (MI),通常使用一个忽略停止治疗的治疗策略策略.
- 这种方法可能会引入偏见,如果结果在停药后有所不同,或者如果缺失的数据在停药者中更为普遍.
研究的目的:
- 提出和评估新的多重归算 (MI) 模型,旨在适应治疗中止前后患者结果的差异.
- 为了评估这些MI模型的性能,在为呼吸道疾病规划第三期临床试验的背景下.
- 将拟议的MI模型与传统分析进行比较,这些分析忽略了治疗中止.
主要方法:
- 开发一套多重归算 (MI) 模型,能够处理间流事件,特别是治疗中止.
- 评估这些MI模型使用模拟数据在计划的第三阶段呼吸系统疾病试验的框架内.
- 通过传统方法引入的偏差和方差与拟议的MI模型进行比较.
主要成果:
- 忽视停止治疗的分析可能会导致治疗效应的大幅偏差和低估治疗效应的可变性.
- 拟议的多重归算 (MI) 模型表明,能够纠正因治疗中止而引入的偏差.
- 在纠正偏差的同时,拟议的MI模型不可避免地导致估计治疗效果的变量增加.
结论:
- 拟议的多重归算 (MI) 模型比传统分析提供了改进,这些分析在临床试验中忽略了治疗中止.
- 选择最佳的MI模型取决于特定的试验特征,包括试验设计,疾病背景,以及停药后观察到和缺失数据的模式.
- 仔细考虑MI模型选择对于准确估计在间流事件的存在下治疗效应至关重要.
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