进行贝叶斯的多臂试验,对CSPN药物的药物的比较有效性进行响应适应性随机化
Alexandra R Brown1, Byron J Gajewski1, Dinesh Pal Mudaranthakam1
1Department of Biostatistics & Data Science, The University of Kansas Medical Center, Kansas City, KS, USA.
Contemporary clinical trials communications
|November 15, 2023
概括
在PAIN-CONTRoLS试验中,贝叶斯响应适应随机化有利于对密码感应性多神经病痛治疗的nortriptyline和duloxetine. 这种适应性设计有效地将参与者分配给更有效的治疗方法.
科学领域:
- 临床试验 临床试验
- 生物统计学 生物统计学
- 药理学 药理学是指药理学的学科.
背景情况:
- 响应适应性随机化在适应性试验设计中越来越多地使用,但执行细节很少.
- 这些设计通过引导更多参与者获得有益的治疗方法来提高患者参与度.
- 贝叶斯方法非常适合适应性设计,因为它能够连续更新信息.
研究的目的:
- 为了说明贝叶斯响应自适应随机化的实际执行.
- 提供支持贝叶斯适应性随机试验的文献.
- 分析加密感官多神经病 (CSPN) 的治疗疗效和耐受性.
主要方法:
- 疼痛控制试验使用贝叶斯响应适应随机化对四种药物进行治疗:nortriptyline,duloxetine,pregabalin和mexiletine.
- 一个实用函数结合了戒烟和疗效率来指导自适应性随机化.
- 临时分析允许提前停止或调整随机化概率.
主要成果:
- 发生了7个随机化适应,增加了对诺特林和杜洛西丁的分配.
- 诺特利普提林和杜洛西丁显示出更低的中断率和更高的疗效.
- 与普雷加巴林相比,梅克西莱丁的退学率最高,但效率更高.
结论:
- 贝叶斯反应适应性随机化是一个有价值的工具,特别是在贝叶斯分析中.
- 疼痛控制试验证明了贝叶斯适应设计的成功实施.
- 这项研究为进行贝叶斯反应适应性随机试验提供了必要的文献.
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