混合的古典贝叶斯式方法来确定样本大小的两臂优势临床试验
1Dipartimento di Scienze Statistiche, Sapienza University of Rome, Piazzale Aldo Moro n. 5, 00185 Roma, Italy.
本研究介绍了一种混合的古典贝叶斯方法,用于在优越性试验中确定样本大小 (SSD). 它通过正式纳入未知参数的不确定性来改进传统的功率分析.
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
- 统计推理 统计推理
背景情况:
- 传统的样本大小确定 (SSD) 依赖于电力分析中未知参数的固定值或估计值.
- 现有的方法可能无法完全解释这些参数的不确定性.
- 混合方法提供了一种方法,可以在频率主义框架内整合先前信息.
研究的目的:
- 在双臂优势试验中,为SSD提出一种新的混合经典-贝叶斯程序.
- 正式纳入对影响统计能力的未知参数的不确定性.
- 要区分使用预先分布的设计期望与初步估计建模.
主要方法:
- 为SSD开发一个混合的古典贝叶斯程序.
- 用二进制数据对双臂优势试验的应用.
- 使用比例差异,日志相对风险和日志赔率比率来推导混合标准.
主要成果:
- 拟议的混合程序允许在未知参数中正式纳入不确定性.
- 使用不同的先前分布来反映设计预期和初步估计的不确定性.
- 该方法用二进制结果的数值示例来说明.
结论:
- 混合经典-贝叶斯方法为SSD在优越性试验中提供了更强大的方法.
- 它为处理参数不确定性提供了一个灵活的框架.
- 由此产生的标准在临床试验设计中具有实用性.
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