在结合真实世界数据时,采用倾向性评分结合的自适应设计方法
Nelson Lu1, Wei-Chen Chen1, Heng Li1
1Division of Biostatistics, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA.
Pharmaceutical statistics
|November 28, 2023
概括
这项研究扩展了适应性临床试验设计的倾向性得分集成复合概率 (PSCL) 方法. 它可以使用现实数据 (RWD) 进行早期的成功索赔或样本大小重新估计.
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
- 现实世界的数据分析.
背景情况:
- 现实世界数据 (RWD) 可以增强前性临床研究.
- 倾向分数集成复合概率 (PSCL) 方法通过形成基于倾向分数 (PS) 和下加权RWD信息的层次来整合RWD.
- PSCL最初是为固定研究设计而开发的.
研究的目的:
- 将PSCL方法扩展到适应性设计框架.
- 在使用RWD的临床试验中,使得早期的成功声明或样本大小重新估计成为可能.
- 在PSCL中提出实施这些适应性特征的总体策略.
主要方法:
- 将PSCL方法扩展到适应性设计.
- 用费舍尔的组合测试用于早期成功的说法.
- 应用Cui,Hung和Wang测试用于重新估计样本大小.
主要成果:
- 提出了一个适应性PSCL的总体策略.
- 早期成功索赔程序的演示.
- 对样本大小重新估计程序的演示.
结论:
- 拟议的自适应性PSCL战略有效地整合了RWD,以改善临床试验设计.
- 这些方法允许在试验进行时具有灵活性,包括提前停止或调整样本大小.
- 该方法为在适应性临床试验中利用RWD提供了一个强大的框架.
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