数字双胞胎和贝叶斯动态借款:两种用于整合历史控制数据的近期方法
Carl-Fredrik Burman1,2, Erik Hermansson1, David Bock1
1Early Biometrics & Statistical Innovation, Data Science & Artificial Intelligence, R&D, AstraZeneca, Gothenburg, Sweden.
Pharmaceutical statistics
|March 4, 2024
概括
贝叶斯动态借用 (BDB) 和数字双胞胎 (DT) 在临床试验中使用历史数据. 虽然DT看起来有前途,但BDB可能会增加1型错误,需要对现实世界的应用进行仔细考虑.
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
- 医疗信息学 医疗信息学
背景情况:
- 越来越多的人对使用外部对照数据进行随机临床试验 (RCT) 感兴趣.
- 潜在的好处包括成本降低,试验持续时间缩短以及适用于小人群的可行性.
- 贝叶斯动态借用 (BDB) 和数字双胞胎 (DT) 是利用历史数据的新兴方法.
研究的目的:
- 分析和比较贝叶斯动态借用 (BDB) 和数字双胞胎 (DT) 方法用于RCT.
- 通过分析推导和模拟来评估它们的性能.
- 识别基本差异以及它们的应用的实际考虑.
主要方法:
- 使用了分析推导和模拟研究.
- 研究了贝叶斯的动态借贷 (BDB) 方法.
- 分析了数字双胞胎 (DT) 方法,利用ANCOVA框架内的历史数据的预后得分.
主要成果:
- BDB和DT都旨在利用历史数据,但具有不同的潜在机制.
- 对于BDB而言,发现的一个重大问题是1型错误率的潜在通货膨胀.
- 在实际的随机临床试验中,DT的实际好处需要进一步的经验验验证.
结论:
- 尽管BDB和DT的目标相似,但它们存在关键差异,这影响了它们对特定RCT的适用性.
- 1型错误通货膨胀是BDB的一个显著缺点,需要谨慎管理.
- 需要进一步的研究和证据来确定DT在现实世界临床试验环境中的实际价值和可靠性.
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