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临床结果评估的有意义的患者内部变化:基于模型的方法与累积分布函数相比
Jinma Ren1, Andrew G Bushmakin2, Paul R Cislo3
1Statistical Research & Data Science Center, Pfizer Inc, Collegeville, Pennsylvania, USA.
Journal of biopharmaceutical statistics
|November 20, 2023
概括
基于模型的方法在临床结果评估 (COA) 中为有意义的患者内变化 (MWPC) 提供了比实证累积分布函数 (eCDF) 曲线更精确的估计. 这是因为它们整合了更多多样化的COA和得分信息.
科学领域:
- 临床试验方法论 临床试验方法论
- 医疗保健中的统计分析.
- 患者报告的结果.
背景情况:
- 美国食品和药物管理局建议以基方法和经验累积分布函数 (eCDF) 曲线为基础,在临床结果评估 (COA) 中确定有意义的患者内变化 (MWPC).
- 基于模型的方法和eCDF曲线之间可能会出现差异,即使使用,也需要对它们的比较性能进行调查.
研究的目的:
- 调查和比较基于的基于模型的方法与eCDF曲线来估计MWPC.
- 了解数据分布如何影响这两种估计方法之间的一致性.
主要方法:
- 采用重复测量模型 (RMM) 和eCDF方法来估计MWPC.
- 使用了现实生活 (NCT02697773) 和模拟数据集,包括多次患者访问和COA/尺度.
- 使用启动链方法计算了MWPC的95%置信区间.
主要成果:
- 数据分布显著影响了RMM和eCDF估计之间的一致性;正常分布的模拟数据显示出比真实临床数据更高的一致性.
- 与RMM相比,eCDF方法产生了更广泛的置信区间和更可变的点估计.
- RMM在MWPC估计中表现出更高的精度.
结论:
- 基于模型的方法通过整合更广泛的COA和得分来提供更精确的MWPC估计.
- 基于模型的方法比eCDF在解释MWPC方面具有优势,因为它们增强了信息集成和精度.
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