用未知形状参数的韦布尔分布对单臂临床试验进行样本大小重新估计和贝叶斯预测概率,使用时间到事件终点,使用未知形状参数的韦布尔分布
Muhammad Waleed1, Jianghua He2, Milind A Phadnis2
1Biostatistics and Research Decision Sciences, Merck & Co, Inc, North Wales, Pennsylvania, United States.
Journal of biopharmaceutical statistics
|August 7, 2023
概括
本研究使用内部试点研究 (IPS) 来调整生存数据分析的样本大小,并使用贝叶斯预测概率来预测在未知韦布尔分布参数的情况下早期临床试验停止决策.
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
- 生存分析的分析.
背景情况:
- 准确的样本大小估计对于研究功率至关重要.
- 生存数据分布参数的不确定性使规划变得复杂.
- 早期停止规则可以提高临床试验的效率.
研究的目的:
- 评估内部试点研究 (IPS) 方法用于在韦布尔生存分析中重新估计样本大小.
- 介绍贝叶斯预测概率方法,用于单臂试验中未知韦布尔形状参数的中间分析.
- 根据有效性或徒劳性,为早期终止试验提供证据.
主要方法:
- 研究了内部试点研究 (IPS) 方法对样本大小调整的有用性.
- 开发了贝叶斯预测概率计算,用于中间分析.
- 使用Weibull分布用于时间到事件终点.
- 建议使用后置模式或形状参数全后置分布的方法.
主要成果:
- IPS方法可以挽救研究功率,但可能会使所需样本大小增加一倍.
- 贝叶斯预测概率计算是可行的,即使有一个未知的韦布尔形状参数.
- 建议将整个形状参数的后部分布纳入,以管理不确定性.
结论:
- IPS方法提供了灵活性,但需要仔细考虑实际约束.
- 贝叶斯预测概率为使用韦布尔生存数据的临床试验中临时决策提供了强大的框架.
- 考虑形状参数的不确定性对于可靠的中间分析至关重要.
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