使用Bernoulli-gated层次混合专家模型的子组分析
Wei Li1, Shanshan Luo2, Yangbo He3
1Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China.
Statistics in medicine
|August 27, 2023
概括
这项研究引入了一种新的小组分析方法,以确定受益于特定治疗的患者群体. 该方法有效地揭示了差异性治疗效应,改进了现有技术.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 识别受益于特定治疗的子群体对于个性化医学至关重要.
- 现有的方法可能无法完全捕捉不同患者群体的治疗效果异质性.
研究的目的:
- 提出和验证一个新的方法,用于分组分析,以确定差异化治疗效应.
- 开发一种可靠的方法来探索治疗反应中的异质性.
主要方法:
- 引入了Bernoulli-gated专家层次混合 (BHME),一种二进制树结构模型.
- 开发了一个基于EM的最大概率方法,用于模型优化.
- 实施了基于测试的后选步骤,以加强对效应异质性的检测.
主要成果:
- 证明了BHME模型的可识别性.
- 拟议的方法在发现差异化治疗效果方面优于竞争方法.
- 成功地将该方法应用于田纳西州的学生/教师成绩比率数据集.
结论:
- 拟议的BHME模型与后选有效地识别了具有差异性治疗效应的子组.
- 与现有方法相比,这种方法可以更好地发现治疗异质性.
- 该方法在现实世界健康结果研究中具有实际应用.
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