对连续生物标记物的贝叶斯适应性丰富设计
Yue Tu1, Yusha Liu2, Wendy J Mack1
1Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA.
Statistics in medicine
|September 14, 2025
概括
本研究介绍了癌症治疗中连续生物标志物的适应性临床试验设计. 它通过利用完整的生物标记信息来提高效率和以患者为中心的决策,与简化标记关系的方法不同.
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
- 精准医学是一门精准的医学.
背景情况:
- 在癌症中,精准医学依赖于针对性治疗的预测生物标志物.
- 当前的临床试验设计往往将连续生物标志物二分为二,从而丢失了有价值的信息.
- 现有的方法可能过于简化了复杂的生物标志物治疗相互作用.
研究的目的:
- 为连续生物标志物提出一种新的适应性丰富试验设计.
- 以适应任何效果形状 (线性,非线性,非单调) 的生物标志物.
- 提高癌症临床试验的统计效率和以患者为中心的决策.
主要方法:
- 开发一个贝叶斯标记-适应性随机化设计.
- 在没有前期二分化的情况下处理连续预测生物标志物.
- 与缺乏自适应随机化的自适应切点选择方法进行比较.
主要成果:
- 拟议的设计有效地处理具有不同效果形状的连续生物标志物.
- 它在做出特定标志物试验决策时实现了高效率.
- 与简化切割点选择方法相比,表现出更好的性能.
结论:
- 适应性丰富设计与贝叶斯标记-适应性随机化对连续生物标记者是有效的.
- 这种方法保留了来自连续生物标志物的信息,从而导致更好的试验结果.
- 为临床试验中的传统生物标志物处理提供了更加复杂和以患者为中心的替代方案.
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