在存在共变量时利用贝叶斯层次模型和倾向评分来利用外部证据
Xiaotian Chen1, Yi Yao1, Li Wang1
1Data and Statistical Sciences, AbbVie Inc., 1 N Waukegan Rd, North Chicago, IL 60064, USA.
Contemporary clinical trials
|July 19, 2023
概括
这项研究引入了新的贝叶斯方法,将外部数据整合到临床试验中,提高效率并解决诸如结果异质性等挑战. 与标准技术相比,拟议的方法提高了估计,功率和错误控制.
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
- 健康 数据科学 数据科学
背景情况:
- 在临床开发中利用外部数据提高了试验效率,并减轻了伦理和招聘问题.
- 增加与外部数据并发的研究控制臂需要考虑结果异质性 (先前数据冲突).
- 预测共变量在结果建模中至关重要,以防止偏差和效率损失.
研究的目的:
- 提出贝叶斯的等级建模策略,同变量调整的元分析预测方法 (cMAP).
- 引入基于倾向性得分 (PS) 的顺序程序,将cMAP整合到可靠的临床试验设计中.
- 在外部数据集成中解决先前数据冲突和共变效应.
主要方法:
- 开发了一种贝叶斯的等级建模策略,结合了同变量调整的元分析预测方法 (cMAP).
- 引入了一个基于倾向分数 (PS) 的顺序程序,集成cMAP.
- 采用模拟研究来评估与标准方法对比拟的方法.
主要成果:
- 拟议的基于cMAP和PS的顺序程序在估计和功率方面表现出卓越的性能.
- 新的方法显示,与单独的PS匹配等标准方法相比,I型错误控制得到了改进.
- 模拟结果证实了结合共变量和解决先前数据冲突的优势.
结论:
- 拟议的贝叶斯分层建模策略 (cMAP) 和基于PS的集成序列程序为临床试验设计提供了显著的优势.
- 这些方法在使用外部控制数据时有效处理结果异质性和共变量调整.
- 这些发现表明,在使用外部数据的临床试验中,提高了效率,减少了偏差,以及更好的统计控制.
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