半参数生存模型的共变量调整响应适应设计
Ayon Mukherjee1, Sayantee Jana2, Stephen Coad3
1Regulatory Affairs and Drug Development Solutions, IQVIA, Frankfurt, Germany.
Statistical methods in medical research
|November 26, 2024
概括
同变量调整响应适应 (CARA) 设计可以改善患者的治疗分配,而无需分配假设. 新的CARA设计确保了使用比例危险的生存试验的有效统计推断,提高了临床试验的效率.
科学领域:
- 临床试验 临床试验
- 生物统计学 生物统计学
- 生存分析的分析.
背景情况:
- 同变量调整响应适应 (CARA) 设计旨在在临床试验中最大限度地使患者受益.
- 现有的CARA设计通常依赖于参数假设,限制了它们在现实世界中的适用性.
- 加速失效时间 (AFT) 模型为一些CARA设计提供了有效的推断,但在初级分析中很少使用.
研究的目的:
- 开发新的CARA设计,用于不需要分布假设的生存试验.
- 根据比例危险假设,确保有效的统计推断.
- 为了实现多个实验目标的最佳分配方法.
主要方法:
- 拟议的CARA设计规避了分布假设,依赖于比例危险假设.
- 使用共变量调整的双重自适应偏差硬币和共变量调整的高效随机自适应设计,用于患者随机化.
- 采用Cox回归系数的顺序估计,以实现最佳的分配目标.
主要成果:
- 广泛的模拟研究表明,拟议的设计具有有利的操作特性.
- 这些设计成功地实施,重新设计了一项现实生活中的验证性临床试验.
- 新的CARA设计可以在没有参数假设的情况下提供有效的统计推断.
结论:
- 开发的CARA设计为涉及生存结果的适应性临床试验提供了灵活和强大的方法.
- 这些方法通过优化治疗分配来提高临床试验的效率.
- 这些发现支持在生存试验环境中更广泛地采用无假设的适应性设计.
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