在连续实验中对二项参数的贝叶斯估计
1Laboratoires Pierre Fabre, Toulouse, France.
Statistical methods in medical research
|September 7, 2023
概括
本研究引入了贝叶斯的方法来估计组序列试验中的二项式参数. 该方法使用设计依赖的先验,为临床试验分析提供了良好的频率特征的改进估计.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 临床试验方法论 临床试验方法论
背景情况:
- 组序列设计对于适应性临床试验至关重要,允许基于累积数据的早期终止.
- 准确估计治疗效应 (双项参数) 是这些试验中决策的关键.
- 传统方法可能无法在估计过程中充分利用设计信息.
研究的目的:
- 开发和介绍一个客观的贝叶斯方法来估计组序列实验中的二项参数.
- 建立一个理论框架,证明将实验设计纳入贝叶斯先验的合理性.
- 提出一个统一的点和间隔估计方法,具有良好的频率特征.
主要方法:
- 设计依赖先验的推导,包括基于杰弗里斯标准和参考先验理论的先验.
- 设计依赖先验的贝叶斯论证理论框架的开发.
- 在组顺序设计中,在全面估计之前应用一个通用的参考.
主要成果:
- 建议的贝叶斯方法为二项参数提供了准确的点和间隔估计.
- 与现有方法相比,后期估计器显示出有利的频率特征.
- 在三个经典的临床试验设计中分析了先前校正对后期估计的影响.
结论:
- 客观的贝叶斯式方法与设计依赖的先验为组序列实验提供了一个强大的和统一的框架.
- 这种方法在理论上提供了一个合理的,在实践中有利的统计推理替代方案.
- 这种方法被认为是连续试验结束后估计的潜在默认值.
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