一种通用校准的贝叶斯层次化建模方法,用于使用多个终点的篮子试验
Xiaohan Chi1, Ying Yuan1, Zhangsheng Yu2,3
1Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Biometrical journal. Biometrische Zeitschrift
|February 17, 2024
概括
这项研究引入了先进的贝叶斯方法来监测篮子试验,改进了对各种亚型癌症新疗法的评估,使用多个疗效终点进行全面的风险效益分析.
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
- 在瘤学瘤学.
背景情况:
- 篮子试验通过同时评估多种癌症亚型的治疗方法来加速药物开发.
- 传统的篮子试验往往依赖于单一的疗效终点 (例如瘤反应),而这些终点对于针对性疗法或免疫疗法等复杂药物来说是不够的.
- 综合风险益处评估需要评估针对性治疗的多个终点.
研究的目的:
- 扩展校准的贝叶斯层次模型,用于监测具有多个终点的II期篮子试验.
- 开发通用模型,以适应不同的终端类型和信息共享依赖关系.
- 引入适应性收缩参数和模型确定校准方法.
主要方法:
- 提出了两种概括的贝叶斯层次模型:一种隐性变量方法和一种多项-正常层次模型.
- 纳入的收缩参数作为子组同质性统计的函数.
- 开发了一种一般的校准方法来定义收缩参数的功能形式.
主要成果:
- 一般化的层次模型容纳了各种终点类型和依赖结构.
- 研究了拟议模型的理论特性.
- 模拟研究证实,通用监测方法产生了理想的操作特征.
结论:
- 提出的贝叶斯分层建模方法有效地监测了具有多个终点的II期篮子试验.
- 这些方法提供了针对性治疗的风险益处概况的更全面的评估.
- 一般化的方法提高了在瘤药物开发中的篮子试验监测的效率和可靠性.
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