在事件驱动的临床试验中,用于盲目样本大小重新估计的灵活分支模型
Tim Mori1,2,3, Sho Komukai4, Satoshi Hattori4,5
1Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany.
Pharmaceutical statistics
|December 11, 2024
概括
在事件驱动的试验中,新的基于斜线的灵活方法用于盲目样本大小重新估计 (BSSR) 提高了准确性. 这种强有力的方法有助于在初步假设不正确时保持试验时间表和招聘人数.
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
- 生存分析的分析.
背景情况:
- 事件驱动的试验依赖于实现特定数量的事件来保持统计能力.
- 盲目样本大小重新估计 (BSSR) 在规划假设有缺陷时,使用临时数据调整样本大小.
- 目前的BSSR方法通常使用参数模型来进行生存函数推断,这可能是不充分的.
研究的目的:
- 为事件驱动试验引入和评估基于spline的灵活BSSR方法.
- 将拟议方法的性能与传统的参数方法进行比较.
- 提高样本大小调整的准确性,改善试验完成时间.
主要方法:
- 开发了一种BSSR程序,利用罗伊斯顿-帕马尔线模型来进行生存函数推算.
- 进行了模拟研究,以比较基于spline的方法与参数模型.
- 将拟议的方法应用于二级渐进性多发性硬化症的现实世界临床试验.
主要成果:
- 灵活的基于spline的BSSR方法表现出稳定性,避免过度或低估参数方法所看到的预期事件.
- 模拟表明基于线的方法的性能优越.
- 该方法的有效性在临床试验应用中得到证实.
结论:
- 拟议的灵活的基于spline的BSSR方法为以错误的规划假设为基础的事件驱动试验提供了更可靠的方法.
- 这种强大的方法使更准确的招聘调整成为可能,帮助试验按计划完成.
- 基于线的推断为BSSR的标准参数模型提供了有价值的替代方案.
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