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随机临床试验随机临床试验的贝叶斯响应自适应随机化与连续的结果:共变调整的作用

Vahan Aslanyan¹, Trevor Pickering¹, Michelle Nuño^1,2

¹Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.

Pharmaceutical statistics

|October 24, 2024

概括

贝叶斯响应自适应随机化 (RAR) 有效地将更多的参与者分配给优越的治疗方法. 建议在小型研究中进行对共变量调整的RAR (CARA),以尽量减少共变量失衡,并确定有前途的治疗方法.

科学领域:

临床试验的设计
生物统计学生物统计学
适应性临床试验适应性临床试验

背景情况:

临时分析允许对样本大小,徒劳性和安全性进行试验修改.
贝叶斯响应自适应随机化 (RAR) 优化了治疗分配,但可能导致共变异失衡.

研究的目的:

为了评估贝叶斯式RAR,并没有对连续结果进行共变量调整.
为了比较适应性试验中临时分析的回归和混合模型.
在不同的RAR方法和场景下评估共变异不平衡.

主要方法:

模拟的临床试验使用贝叶斯的RAR和对共变量调整的RAR (CARA).
用变化得分和重复测量分析的连续结果.
使用回归或混合模型建模的中间分析.
分配比例和共同变量不平衡的比较.

主要成果:

两种RAR版本都将更多的参与者分配给更好的治疗方法,而不是平等的随机化.
使用混合模型的动态分配显示了较小的分配方差和与回归模型相似的共同变量失衡.
在具有低协同变量流行率的小样本大小中,CARA显示了最小的协同变量失衡.

结论:

贝叶斯式RAR有效地引导参与者获得更优质的治疗方法.
与共变量调整后的RAR (CARA) 对于小型试点研究特别有益.
CARA将共变异不平衡降至最低,有助于选择用于确认试验的候选治疗方法.

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