基于几何平均危险比率的混合物模型的样本大小计算及其对非比例危险的应用
Zixing Wang1, Qingyang Zhang1, Allen Xue1
1Kite, a Gilead company, Santa Monica, California, USA.
Pharmaceutical statistics
|December 28, 2023
概括
癌症免疫疗法试验往往显示出不成比例的危险,挑战传统分析. 本研究介绍了一个扩展的混合物模型和几何平均危险比率 (gAHR),用于在复杂的生存场景中准确的样本大小和功率计算.
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
- 癌症研究 癌症研究
背景情况:
- 癌症免疫疗法引入了独特的生存模式,如延迟效果和治愈率.
- 这些模式违反比例危险假设,使传统试验分析复杂化.
- 现有的方法,如治愈率模型,部分解决这些复杂性.
研究的目的:
- 在生存分析中扩展混合物模型来处理多个不成比例的危险模式.
- 开发一个几何平均危险比率 (gAHR) 来量化治疗效应.
- 为了获得准确的样本大小和功率公式,用于临床试验与不成比例的危险.
主要方法:
- 扩展混合模型,以适应多个不成比例的生存模式.
- 开发几何平均危险比率 (gAHR) 用于治疗效果量化.
- 根据日志等级测试的非中心性参数推导样本大小和功率公式.
主要成果:
- 拟议的方法在各种非比例场景的模拟中显示出与比例危险模型相比的显著优势.
- 真实试验数据的混合建模显示了使用生物标志物和早期疗效的先前信息的实际应用.
- 与基于模拟的设计相比,新方法为功率和样本大小计算提供了更有效和更准确的方法.
结论:
- 扩展混合模型和gAHR为分析复杂的生存数据的癌症免疫疗法试验提供了强大的框架.
- 由此得出的样本大小和功率公式提高了临床试验设计的效率和准确性.
- 这种方法有望促进癌症免疫治疗研究中的创新试验设计.
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