药物开发 药物开发
Coco Kusiak1, Rachel Mak-McCully1, Carolyn Murray1
1Unlearn.AI, San Francisco, CA, USA.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 25, 2025
概括
数字双胞胎可以将阿尔茨海默病试验样本大小降低高达24%,或增加二级终点的功率. 这种方法提高了临床试验中的统计效率,用于像donanemab.com这样的治疗方法.
科学领域:
- 神经科学是一个神经科学.
- 临床试验 临床试验
- 生物统计学 生物统计学
背景情况:
- 在TRAILBLAZER-ALZ 2试验中,donanemab有效地减少了粉样质斑块,并减缓了早期阿尔茨海默病的认知衰退.
- 然而,二次终点和子组分析的强度不足.
- 预测共变量,就像数字双胞胎一样,可以提高统计能力并减少样本大小要求.
研究的目的:
- 为了模拟 TRAILBLAZER-ALZ 2 试验,并量化纳入参与者数字双胞胎的样本大小减少或功率增加.
- 评估数字双胞胎对阿尔茨海默病临床试验数据统计分析的影响.
主要方法:
- 创建了一个合成数据集,反映了TRAILBLAZER-ALZ 2队列,与参与者随机分别为1:1.
- 模拟了多纳尼马布治疗效果.
- 参与者的数字双胞胎是使用Unlearn的阿尔茨海默病数字双胞胎生成器生成的.
主要成果:
- 将数字双胞胎作为共变量纳入可能会使 TRAILBLAZER-ALZ 2 控制臂在主要结果 (18 个月后的 iADRS) 中减少高达 24%.
- 数字双胞胎可以将二级终点的统计能力提高高达15%.
- 亚组分析也显示了功率的增加.
结论:
- 数字双胞胎,当作为超级变量使用时,可以显著减少样本大小或提高阿尔茨海默病临床试验的功率.
- 这种方法提高了分析初级,二级和子组终点的效率.
- 数字双胞胎为优化阿尔茨海默氏症研究中的临床试验设计和分析提供了宝贵的工具.
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