在多臂随机对照试验中探索贝叶斯适应设计,使用患者偏好臂进行随机对照试验.
Alexandra R Brown1, Byron J Gajewski1, Dinesh Pal Mudaranthakam1
1Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, USA.
Journal of biopharmaceutical statistics
|December 23, 2025
概括
贝叶斯适应性设计 (BAD) 与响应适应性随机化 (RAR) 和早期停止是最有效的临床试验设计. 这些适应性方法改善了参与者的结果和试验效率,以便在未来进行研究.
科学领域:
- 临床试验的设计
- 生物统计学 生物统计学
- 药物经济学 药物经济学
背景情况:
- 临床试验效率对于资源优化和及时结果至关重要.
- 多臂试验和适应性设计,包括响应适应性随机化 (RAR) 和早期停止,提高试验效率.
- 适应性设计提供了潜在的好处,例如增加了对优质治疗的参与者分配和更早的试验终止.
研究的目的:
- 为未来的研究确定最有效的临床试验设计.
- 使用模拟来比较固定和贝叶斯适应设计 (BAD) 的性能.
- 为了评估多可萨赫萨酸 (DHA) 配方在孕妇中达到最佳的坚持.
主要方法:
- 使用模拟来比较固定和贝叶斯适应设计 (BAD).
- 评估了各种试验设计的操作特性,包括RAR和早期停止.
- 使用数据包裹分析 (DEA) 来评估基于功率,样本大小和预期结果的设计效率.
主要成果:
- 贝叶斯适应性设计 (BAD) 结合RAR和早期停止成功或徒劳,证明了卓越的效率.
- DEA框架提供了一种用于评估和平衡多个效率指标的新方法.
- 在试验设计的背景下,考虑了不同的DHA配方 (控制,,囊,患者选择).
结论:
- 贝叶斯适应性设计 (BAD) 与响应适应性随机化 (RAR) 和早期停止是最有效的研究设计.
- 适应性策略可以提高试验效率和参与者利益.
- 这项研究为选择最佳临床试验设计提供了一个框架.
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