利用机器学习:共变量调整的贝叶斯适应性随机化和在多臂生存试验中发现子组
Wenxuan Xiong1, Jason Roy1, Hao Liu2
1Department of Biostatistics and Epidemiology, Rutgers University School of Public Health, Piscataway, NJ, USA.
Contemporary clinical trials
|April 30, 2024
概括
这项研究引入了一种新的贝叶斯适应性设计,用于临床试验,以个性化治疗. 它确定了从特定疗法中获益最多的患者子组,改善了治疗选择和试验功率.
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
- 个性化医疗是个性化的医疗.
背景情况:
- 临床试验对于评估治疗的安全性和有效性至关重要.
- 个性化医疗需要了解治疗效果如何在患者子组之间变化,通常由生物标志物定义.
- 现有的试验设计可能无法充分解决患者异质性问题,这可能会阻碍针对特定个体确定最佳治疗方法.
研究的目的:
- 引入一种新的贝叶斯适应设计,用于具有时间到事件终点的多臂临床试验.
- 通过结合因果效应估计和识别具有差异治疗益处的患者子组来提高治疗分配.
- 在个性化医学时代,提高临床试验的精度和效率.
主要方法:
- 建议采用一种对共变量调整的响应适应性随机化策略.
- 使用由随机拦截加速失效时间 (BART) 模型推导的因果效应估计来更新治疗分配概率.
- 在试验后使用多反应决策树来识别具有不同治疗影响的子组.
主要成果:
- 拟议的贝叶斯适应性设计在识别对治疗有不同反应的患者子组方面表现出灵活性.
- 与共变量调整的随机化保持了试验功率,同时适应新出现的治疗效果数据.
- 模拟证实了设计能够精确地确定从特定干预中获益最多的子组的能力.
结论:
- 开发的贝叶斯适应设计为现代临床试验提供了强大而灵活的方法.
- 这种设计通过识别具有差异治疗效果的患者子组,促进了个性化医疗.
- 该方法提高了根据个体特征选择最佳治疗的潜力.
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